cshalizi + re:homophily_and_confounding   125

[1908.00849] Voter model on networks partitioned into two cliques of arbitrary sizes
"The voter model is an archetypal stochastic process that represents opinion dynamics. In each update, one agent is chosen uniformly at random. The selected agent then copies the current opinion of a randomly selected neighbour. We investigate the voter model for a network with an exogenous community structure: two cliques (i.e. complete subgraphs) randomly linked by X interclique edges. We show that, counterintuitively, the mean consensus time is typically not a monotonically decreasing function of X. Cliques of fixed proportions with opposite initial opinions reach a consensus, on average, most quickly if X scales as N3/2, where N is the number of agents in the network. Hence, to accelerate a consensus between cliques, agents should connect to more members in the other clique as N increases but not to the extent that cliques lose their identity as distinct communities. We support our numerical results with an equation-based analysis. By interpolating between two asymptotic heterogeneous mean-field approximations, we obtain an equation for the mean consensus time that is in excellent agreement with simulations for all values of X."
to:NB  voter_model  networks  gastner.michael_t.  stochastic_processes  re:homophily_and_confounding 
10 weeks ago by cshalizi
[1709.10024] Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach
"We propose a method of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function approach. We leave the functional form of the control function unspecified and treat it as unknown. To estimate the model, we use a sieve semiparametric approach, and we establish asymptotics of the semiparametric estimator."
to:NB  causal_inference  social_networks  social_influence  re:homophily_and_confounding 
10 weeks ago by cshalizi
[1906.09076] Inside the Echo Chamber: Disentangling network dynamics from polarization
"Echo chambers are defined by the simultaneous presence of opinion polarization with respect to a controversial topic and homophily, i.e. the preference of individuals to interact with like-minded peers. While recent efforts have been devoted to detecting the presence of echo chambers in polarized debates on online social media, the dynamics leading to the emergence of these phenomena remain unclear. Here, we contribute to this endeavor by proposing novel metrics to single out the effect of the network dynamics from the opinion polarization. By using a Twitter data set collected during a controversial political debate in Brazil in 2016, we employ a temporal network approach to gauge the strength of the echo chamber effect over time. We define a measure of opinion coherence in the network showing how the echo chamber becomes weaker across the observed period. The analysis of the hashtags diffusion in the network shows that this is due to the increase of social interactions between users with opposite opinions. Finally, the analysis of the mutual entropy between the opinions expressed and received by the users permits to quantify the social contagion effect. We find empirical evidence that the polarization of the users and the dynamics of their interactions may evolve independently. Our findings may be of interest to the broad array of researchers studying the dynamics of echo chambers and polarization in online social networks."
to:NB  social_media  social_life_of_the_mind  social_influence  re:homophily_and_confounding 
june 2019 by cshalizi
Testing and Estimation of Social Network Dependence With Time to Event Data: Journal of the American Statistical Association: Vol 0, No 0
"Nowadays, events are spread rapidly along social networks. We are interested in whether people’s responses to an event are affected by their friends’ characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person’s survival time might be affected by his or her friends’ features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement."
to:NB  network_data_analysis  survival_analysis  statistics  social_influence  re:homophily_and_confounding 
june 2019 by cshalizi
Peer Influence and Delinquency | Annual Review of Criminology
"Peer influence occupies an intriguing place in criminology. On the one hand, there is a long line of theorizing and empirical work highlighting it as a key causal process for delinquency. On the other, there is a group of theoretical skeptics who view it as one of the most notorious examples of a spurious link. After discussing these perspectives, this review takes stock of our intellectual advancements in understanding peer influence over decades' worth of research toward this endeavor. We conclude that although there have been important gains, essential questions and gaps remain. Toward this aim, we offer some lines of future work that we believe offer pathways to yielding the greatest added value to the discipline."
to:NB  social_influence  crime  re:homophily_and_confounding 
may 2019 by cshalizi
[1705.08527] Causal inference for social network data
"We extend recent work by van der Laan (2014) on causal inference for causally connected units to more general social network settings. Our asymptotic results allow for dependence of each observation on a growing number of other units as sample size increases. We are not aware of any previous methods for inference about network members in observational settings that allow the number of ties per node to increase as the network grows. While previous methods have generally implicitly focused on one of two possible sources of dependence among social network observations, we allow for both dependence due to contagion, or transmission of information across network ties, and for dependence due to latent similarities among nodes sharing ties. We describe estimation and inference for causal effects that are specifically of interest in social network settings."
to:NB  to_read  heard_the_talk  causal_inference  network_data_analysis  kith_and_kin  ogburn.elizabeth  van_der_laan.mark  re:homophily_and_confounding  to_teach:baby-nets 
april 2019 by cshalizi
[1904.02308] Randomization tests for peer effects in group formation experiments
"Measuring the effect of peers on individual outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable ways. Group formation experiments avoid this problem by randomly assigning individuals to groups and observing their responses; for example, do first-year students have better grades when they are randomly assigned roommates who have stronger academic backgrounds? Standard approaches for analyzing these experiments, however, are heavily model-dependent and generally fail to exploit the randomized design. In this paper, we extend methods from randomization-based testing under interference to group formation experiments. The proposed tests are justified by the randomization itself, require relatively few assumptions, and are exact in finite samples. First, we develop procedures that yield valid tests for arbitrary group formation designs. Second, we derive sufficient conditions on the design such that the randomization test can be implemented via simple random permutations. We apply this approach to two recent group formation experiments."
to:NB  statistics  causal_inference  experimental_sociology  re:homophily_and_confounding 
april 2019 by cshalizi
[1902.04114] Using Embeddings to Correct for Unobserved Confounding
"We consider causal inference in the presence of unobserved confounding. In particular, we study the case where a proxy is available for the confounder but the proxy has non-iid structure. As one example, the link structure of a social network carries information about its members. As another, the text of a document collection carries information about their meanings. In both these settings, we show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks and text both admit high-quality embedding models that can be used for this semi-supervised prediction. Our method yields valid inferences under suitable (weak) conditions on the quality of the predictive model. We validate the method with experiments on a semi-synthetic social network dataset. We demonstrate the method by estimating the causal effect of properties of computer science submissions on whether they are accepted at a conference."
to:NB  causal_inference  statistics  blei.david  re:homophily_and_confounding  to_read 
february 2019 by cshalizi
Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties - Jacob C. Fisher, 2019
"Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model—representing the distances in social space—or a series of networks drawn from those probabilities—representing routine churn in the network—are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, the author explores the properties of the model. The author shows that the model produces smoothed diffusion results, which predict attitudes in future waves 10 percent better than a diffusion model using the observed network and up to 5 percent better than diffusion models using alternative, non-model-based smoothing approaches."
to:NB  to_read  social_influence  social_networks  network_data_analysis  re:homophily_and_confounding  to_teach:baby-nets  via:gabriel_rossman 
february 2019 by cshalizi
[1811.10372] Disentangling sources of influence in online social networks
"Information propagation in online social networks is facilitated by two types of influence - endogenous (peer) influence that is dependent on the network structure and current state of each user and exogenous (external) which is independent of these. However, inference of these influences from data remains a challenge. In this paper we propose a methodology that yields estimates of both endogenous and exogenous influence using only a social network structure and a single activation cascade. We evaluate our methodology on simulated activation cascades as well as on cascades obtained from several large Facebook political survey applications. We show that our methodology is able to provide estimates of endogenous and exogenous influence in online social networks, characterize activation of each individual user as being endogenously or exogenously driven, and to identify most influential groups of users."
to:NB  to_read  contagion  social_influence  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
december 2018 by cshalizi
[1809.10302] The hidden traits of endemic illiteracy in cities
"In spite of the considerable progress towards reducing illiteracy rates, many countries, including developed ones, have encountered difficulty achieving further reduction in these rates. This is worrying because illiteracy has been related to numerous health, social, and economic problems. Here, we show that the spatial patterns of illiteracy in urban systems have several features analogous to the spread of diseases such as dengue and obesity. Our results reveal that illiteracy rates are spatially long-range correlated, displaying non-trivial clustering structures characterized by percolation-like transitions and fractality. These patterns can be described in the context of percolation theory of long-range correlated systems at criticality. Together, these results provide evidence that the illiteracy incidence can be related to a transmissible process, in which the lack of access to minimal education propagates in a population in a similar fashion to endemic diseases."

--- Of course it's coming out in _Physica A_.
to:NB  sociology  to_be_shot_after_a_fair_trial  social_influence  re:homophily_and_confounding 
september 2018 by cshalizi
Centola, D.: How Behavior Spreads: The Science of Complex Contagions (Hardcover and eBook) | Princeton University Press
"New social movements, technologies, and public-health initiatives often struggle to take off, yet many diseases disperse rapidly without issue. Can the lessons learned from the viral diffusion of diseases be used to improve the spread of beneficial behaviors and innovations? In How Behavior Spreads, Damon Centola presents over a decade of original research examining how changes in societal behavior--in voting, health, technology, and finance—occur and the ways social networks can be used to influence how they propagate. Centola's startling findings show that the same conditions accelerating the viral expansion of an epidemic unexpectedly inhibit the spread of behaviors.
"While it is commonly believed that "weak ties"—long-distance connections linking acquaintances—lead to the quicker spread of behaviors, in fact the exact opposite holds true. Centola demonstrates how the most well-known, intuitive ideas about social networks have caused past diffusion efforts to fail, and how such efforts might succeed in the future. Pioneering the use of Web-based methods to understand how changes in people's social networks alter their behaviors, Centola illustrates the ways in which these insights can be applied to solve countless problems of organizational change, cultural evolution, and social innovation. His findings offer important lessons for public health workers, entrepreneurs, and activists looking to harness networks for social change."
to:NB  books:noted  contagion  social_influence  social_networks  centola.damon  sociology  re:homophily_and_confounding 
july 2018 by cshalizi
Infectious Disease Modeling of Social Contagion in Networks
"It has recently been suggested that certain, particular types of latent homophily, in which an unobservable trait influences both which friends one chooses and current and future behavior, may be impossible to distinguish from contagion in observational studies and hence may bias estimates of contagion and homophily [50]. The circumstances under which this is likely to be a serious source of bias (e.g., whether people, empirically, behave in these sorts of ways), and what (if anything) might be done about it (absent experimental data of the kind that some new networks studies are providing [22]) merits further study. Observational data invariably pose problems for causal inference, and require one set of assumptions or another to analyze; the plausibility of these assumptions (even of standard ones that are widely used) warrants constant review.
"The SISa model as presented here assumes that all individuals have the same probability of changing state (though not everyone will actually change state within their lifetime). It is clearly possible, however, that there is heterogeneity between individuals in these rates. We do not have sufficient data on obesity in the Framingham dataset to explore this issue, which would require observing numerous transitions between states for each individual. Exploring individual differences in acquisition rate empirically is a very interesting topic for future research, as is extending the theoretical framework we introduce to take into account individual differences."

--- For "suggested", read "proved"; the second paragraph amounts to saying "Let's just agree to ignore this".
to:NB  contagion  epidemic_models  social_influence  re:homophily_and_confounding  christakis.nicholas  via:rvenkat  have_skimmed 
march 2018 by cshalizi
The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health
"Humans tend to form social relationships with others who resemble them. Whether this sorting of like with like arises from historical patterns of migration, meso-level social structures in modern society, or individual-level selection of similar peers remains unsettled. Recent research has evaluated the possibility that unobserved genotypes may play an important role in the creation of homophilous relationships. We extend this work by using data from 5,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarities among pairs of friends. Although there is some evidence that friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), further analysis suggests that meso-level forces, such as school assignment, are a principal source of genetic similarity between friends. We also observe apparent social–genetic effects in which polygenic scores of an individual’s friends and schoolmates predict the individual’s own educational attainment. In contrast, an individual’s height is unassociated with the height genetics of peers."

--- Contributed, hence the last tag.
to:NB  sociology  re:homophily_and_confounding  human_genetics  social_networks  homophily  to_be_shot_after_a_fair_trial 
january 2018 by cshalizi
[1706.04692] Bias and high-dimensional adjustment in observational studies of peer effects
"Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences."
to:NB  to_read  re:homophily_and_confounding  causal_inference  network_data_analysis  eckles.dean  bakshy.eytan  experimental_sociology 
october 2017 by cshalizi
Reach and speed of judgment propagation in the laboratory
"In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor’s judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people’s errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory."
to:NB  social_influence  experimental_psychology  experimental_sociology  re:homophily_and_confounding 
august 2017 by cshalizi
Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide: American Journal of Sociology: Vol 115, No 1
"Most sociological theories consider murder an outcome of the differential distribution of individual, neighborhood, or social characteristics. And while such studies explain variation in aggregate homicide rates, they do not explain the social order of murder, that is, who kills whom, when, where, and for what reason. This article argues that gang murder is best understood not by searching for its individual determinants but by examining the social networks of action and reaction that create it. In short, the social structure of gang murder is defined by the manner in which social networks are constructed and by people's placement in them. The author uses a network approach and incident‐level homicide records to recreate and analyze the structure of gang murders in Chicago. Findings demonstrate that individual murders between gangs create an institutionalized network of group conflict, net of any individual's participation or motive. Within this network, murders spread through an epidemic‐like process of social contagion as gangs evaluate the highly visible actions of others in their local networks and negotiate dominance considerations that arise during violent incidents."

--- Uses the same old methods for detecting contagion as Christakis-Fowler; perhaps more plausible here?
to:NB  have_read  social_networks  violence  contagion  social_influence  sociology  re:network_differences  honor  re:homophily_and_confounding 
august 2017 by cshalizi
[1706.08440] Challenges to estimating contagion effects from observational data
"A growing body of literature attempts to learn about contagion using observational (i.e. non-experimental) data collected from a single social network. While the conclusions of these studies may be correct, the methods rely on assumptions that are likely--and sometimes guaranteed to be--false, and therefore the evidence for the conclusions is often weaker than it seems. Developing methods that do not need to rely on implausible assumptions is an incredibly challenging and important open problem in statistics. Appropriate methods don't (yet!) exist, so researchers hoping to learn about contagion from observational social network data are sometimes faced with a dilemma: they can abandon their research program, or they can use inappropriate methods. This chapter will focus on the challenges and the open problems and will not weigh in on that dilemma, except to mention here that the most responsible way to use any statistical method, especially when it is well-known that the assumptions on which it rests do not hold, is with a healthy dose of skepticism, with honest acknowledgment and deep understanding of the limitations, and with copious caveats about how to interpret the results."
to:NB  have_read  ogburn.elizabeth  contagion  homophily  social_influence  social_networks  causal_inference  statistics  re:homophily_and_confounding 
july 2017 by cshalizi
Connecting in College: How Friendship Networks Matter for Academic and Social Success, McCabe
"We all know that good study habits, supportive parents, and engaged instructors are all keys to getting good grades in college. But as Janice M. McCabe shows in this illuminating study, there is one crucial factor determining a student’s academic success that most of us tend to overlook: who they hang out with. Surveying a range of different kinds of college friendships, Connecting in College details the fascinatingly complex ways students’ social and academic lives intertwine and how students attempt to balance the two in their pursuit of straight As, good times, or both.
"As McCabe and the students she talks to show, the friendships we forge in college are deeply meaningful, more meaningful than we often give them credit for. They can also vary widely. Some students have only one tight-knit group, others move between several, and still others seem to meet someone new every day. Some students separate their social and academic lives, while others rely on friendships to help them do better in their coursework. McCabe explores how these dynamics lead to different outcomes and how they both influence and are influenced by larger factors such as social and racial inequality. She then looks toward the future and how college friendships affect early adulthood, ultimately drawing her findings into a set of concrete solutions to improve student experiences and better guarantee success in college and beyond."
to:NB  books:noted  academia  education  social_networks  re:homophily_and_confounding 
december 2016 by cshalizi
[1607.06565] Controlling for Latent Homophily in Social Networks through Inferring Latent Locations
"Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, i.e., with a node's network partners being informative about the node's attributes and therefore its behavior. We show that {\em if} the network grows according to either a community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. Moreover, these estimates are informative enough that controlling for them allows for unbiased and consistent estimation of social-influence effects in additive models. For community models, we also provide bounds on the finite-sample bias. These are the first results on the consistent estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them."
self-promotion  social_networks  network_data_analysis  causal_inference  community_discovery  re:homophily_and_confounding  to:blog 
july 2016 by cshalizi
[1606.09082] Formation of homophily in academic performance: students prefer to change their friends rather than performance
"Homophily, the tendency of individuals to associate with others who share similar traits, has been identified as a major driving force in the formation and evolution of social ties. In many cases, it is not clear if homophily is the result of a socialization process, where individuals change their traits according to the dominance of that trait in their local social networks, or if it results from a selection process, in which individuals reshape their social networks so that their traits match those in the new environment. Here we demonstrate the detailed temporal formation of strong homophily in academic achievements of high school and university students. We analyze a unique dataset that contains information about the detailed time evolution of a friendship network of 6,000 students across 42 months. Combining the evolving social network data with the time series of the academic performance (GPA) of individual students, we show that academic homophily is a result of selection: students prefer to gradually reorganize their social networks according to their performance levels, rather than adapting their performance to the level of their local group. We find no signs for a pull effect, where a social environment of good performers motivates bad students to improve their performance. We are able to understand the underlying dynamics of grades and networks with a simple model. The lack of a social pull effect in classical educational settings could have important implications for the understanding of the observed persistence of segregation, inequality and social immobility in societies."

--- On a quick skim, they do not actually address the confounding problem (and I wonder if they have actually read their reference [41]).
to:NB  to_read  social_networks  homophily  social_influence  education  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
june 2016 by cshalizi
Separating Homophily and Peer Influence with Latent Space by Joseph P Davin, Sunil Gupta, Mikolaj Jan Piskorski :: SSRN
"We study the impact of peer behavior on the adoption of mobile apps in a social network. To identify social influence properly, we introduce latent space as an approach to control for latent homophily, the idea that “birds of a feather flock together.” In a series of simulations, we show that latent space coordinates significantly reduce bias in the estimate of social influence. The intuition is that latent coordinates act as proxy variables for hidden traits that give rise to latent homophily. The approach outperforms existing methods such as including observed covariates, random effects, or fixed effects. We then apply the latent space approach to identify social influence on installation of mobile apps in a social network. We find that peer influence account for 27% of mobile app adoptions, and that latent homophily inflates this estimate by 40% (to 38%). In some samples, ignoring latent homophily can result in overestimation of social effects by over 100%."
to:NB  have_read  causal_inference  network_data_analysis  statistics  homophily  re:homophily_and_confounding  scooped? 
may 2016 by cshalizi
PLOS ONE: Trickle-Down Preferences: Preferential Conformity to High Status Peers in Fashion Choices
On first skim, they don't really seem to consider that women who move from low to high status locations are probably _already different_ from those who don't...
I can't believe I'm writing this, but this might really be a job for propensity-score matching.
to:NB  to_be_shot_after_a_fair_trial  social_influence  economics  shoes  re:homophily_and_confounding  to_teach:undergrad-ADA 
may 2016 by cshalizi
Identifying Formal and Informal Influence in Technology Adoption with Network Externalities
"Firms introducing network technologies (whose benefits depend on who installs the technology) need to understand which user characteristics confer the greatest network benefits on other potential adopters. To examine which adopter characteristics matter, I use the introduction of a video-messaging technology in an investment bank. I use data on its 2,118 employees, their adoption decisions, and their 2.4 million subsequent calls. The video-messaging technology can also be used to watch TV. Exogenous shocks to the benefits of watching TV are used to identify the causal (network) externality of one individual user's adoption on others' adoption decisions. I allow this network externality to vary in size with a variety of measures of informal and formal influence. I find that adoption by either managers or workers in “boundary spanner” positions has a large impact on the adoption decisions of employees who wish to communicate with them. Adoption by ordinary workers has a negligible impact. This suggests that firms should target those who derive their informal influence from occupying key boundary-spanning positions in communication networks, in addition to those with sources of formal influence, when launching a new network technology."
to:NB  causal_inference  instrumental_variables  diffusion_of_innovations  statistics  social_influence  social_networks  re:homophily_and_confounding  to_be_shot_after_a_fair_trial  via:mcfowland 
march 2016 by cshalizi
[1507.03984] Sensitivity Analysis Without Assumptions
"Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on the causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having a confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one confounder. Without imposing any assumptions on the confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy, but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder."
to:NB  causal_inference  misspecification  partial_identification  statistics  vanderweele.tyler  to_read  re:homophily_and_confounding 
august 2015 by cshalizi
On the Directionality Test of Peer Effects in Social Networks
"One interesting idea in social network analysis is the directionality test that utilizes the directions of social ties to help identify peer effects. The null hypothesis of the test is that if contextual factors are the only force that affects peer outcomes, the estimated peer effects should not differ, if the directions of social ties are reversed. In this article, I statistically formalize this test and investigate its properties under various scenarios. In particular, I point out the validity of the test is contingent on the presence of peer selection, sampling error, and simultaneity bias. I also outline several methods that can help provide causal estimates of peer effects in social networks."

- Last tag applies mostly to the last sentence.
to:NB  to_read  network_data_analysis  causal_inference  social_networks  homophily  social_influence  contagion  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
may 2015 by cshalizi
PLOS ONE: Classic Maya Bloodletting and the Cultural Evolution of Religious Rituals: Quantifying Patterns of Variation in Hieroglyphic Texts
"Religious rituals that are painful or highly stressful are hypothesized to be costly signs of commitment essential for the evolution of complex society. Yet few studies have investigated how such extreme ritual practices were culturally transmitted in past societies. Here, we report the first study to analyze temporal and spatial variation in bloodletting rituals recorded in Classic Maya (ca. 250–900 CE) hieroglyphic texts. We also identify the sociopolitical contexts most closely associated with these ancient recorded rituals. Sampling an extensive record of 2,480 hieroglyphic texts, this study identifies every recorded instance of the logographic sign for the word ch’ahb’ that is associated with ritual bloodletting. We show that documented rituals exhibit low frequency whose occurrence cannot be predicted by spatial location. Conversely, network ties better capture the distribution of bloodletting rituals across the southern Maya region. Our results indicate that bloodletting rituals by Maya nobles were not uniformly recorded, but were typically documented in association with antagonistic statements and may have signaled royal commitments among connected polities."

--- Not a context in which I ever expected to find myself cited.
Also: did they look for other words (glyphs, I guess) that might refer to such sacrifices? They might just be seeing a linguistic difference, rather than one of practices
have_read  maya_civilization  archaeology  social_networks  social_influence  homophily  re:homophily_and_confounding  in_NB  to:blog 
september 2014 by cshalizi
Reconsidering Durkheim's Assessment of Tarde: Formalizing a Tardian Theory of Imitation, Contagion, and Suicide Suggestion - Abrutyn - 2014 - Sociological Forum - Wiley Online Library
"Emile Durkheim summarily rejected Gabriel Tarde's imitation thesis, arguing that sociology need only concern itself with social suicide rates. Over a century later, a burgeoning body of suicide research has challenged Durkheim's claim to a general theory of suicide as 4 decades worth of evidence has firmly established that (1) there is a positive association between the publicization of celebrity suicides and a spike in the aggregate suicide rate, (2) some social environments are conducive to epidemic-like outbreaks of suicides, and (3) suicidal ideas or behavior spreads to some individuals exposed to a personal role model's suicidal behavior—for example, a friend or family member. Revisiting Tarde, the article examines why Tarde's theory deserves renewed attention, elucidates what he meant by imitation, and then formalizes his “laws” into testable theses, while suggesting future research questions that would advance the study of suicide, as well as other pathologies. Each “law” is elaborated by considering advances in contemporary social psychology as well as in light of its ability to supplement Durkheim's theory in explaining the “outlier” cases."
to:NB  sociology  suicide  social_influence  contagion  social_science_methodology  re:homophily_and_confounding 
september 2014 by cshalizi
A simple generative model of collective online behavior
"Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates—even when using purely observational data without experimental design—that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior."
to:NB  to_read  sociology  networked_life  social_influence  re:homophily_and_confounding  to_be_shot_after_a_fair_trial  porter.mason_a. 
july 2014 by cshalizi
Can Achievement Peer Effect Estimates Inform Policy? A View from Inside the Black Box
"Empirical studies of peer effects rely on the assumption that peer spillovers can be measured through observables. However, in the education context, many theories of peer spillovers center around unobservables, such as ability, effort, or motivation. I show that when peer effects arise from unobservables, the typical empirical specifications will not measure these effects accurately, which may help explain differences in the magnitude and even sign of peer effect estimates across studies. I also show that under reasonable assumptions, these estimates cannot be applied to determine the effects of regrouping students, a central motivation of the literature."
to:NB  social_influence  economics  causal_inference  re:homophily_and_confounding  statistics 
july 2014 by cshalizi
Social selection and peer influence in an online social network
"Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes."
to:NB  social_networks  social_influence  homophily  re:homophily_and_confounding  sociology  causal_inference  to_read  to_be_shot_after_a_fair_trial 
july 2014 by cshalizi
Experimental evidence of massive-scale emotional contagion through social networks
"Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people."
to:NB  to_read  social_influence  contagion  experimental_psychology  psychology  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
june 2014 by cshalizi
[1404.0067] Topics in social network analysis and network science
"This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided."

--- From a quick skim, they rather over-value what can be accomplished by rejecting an internally-inconsistent model...
to:NB  to_read  have_skimmed  social_networks  social_influence  network_data_analysis  re:homophily_and_confounding 
april 2014 by cshalizi
Phys. Rev. Lett. 112, 098702 (2014) - Origin of Peer Influence in Social Networks
"Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be."
to:NB  social_influence  social_networks  re:do-institutions-evolve  to_be_shot_after_a_fair_trial  re:homophily_and_confounding 
march 2014 by cshalizi
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
"The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic."
in_NB  epidemic_models  networks  network_data_analysis  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
january 2014 by cshalizi
[1312.6169] Learning Information Spread in Content Networks
"We introduce a model for predicting the diffusion of content information on social media. When propagation is usually modeled on discrete graph structures, we introduce here a continuous diffusion model, where nodes in a diffusion cascade are projected onto a latent space with the property that their proximity in this space reflects the temporal diffusion process. We focus on the task of predicting contaminated users for an initial initial information source and provide preliminary results on differents datasets."
to:NB  network_data_analysis  social_media  social_influence  epidemiology_of_representations  to_read  re:homophily_and_confounding 
january 2014 by cshalizi
[1312.6122] Shadow networks: Discovering hidden nodes with models of information flow
"Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are incomplete. Here we explore how accidentally missing or deliberately hidden nodes may be detected in networks by the effect of their absence on predictions of the speed with which information flows through the network. We use Symbolic Regression (SR) to learn models relating information flow to network topology. These models show localized, systematic, and non-random discrepancies when applied to test networks with intentionally masked nodes, demonstrating the ability to detect the presence of missing nodes and where in the network those nodes are likely to reside."
to:NB  social_networks  network_data_analysis  social_influence  re:homophily_and_confounding  to_read  epidemiology_of_representations 
january 2014 by cshalizi
[1311.2878] Selection Effects in Online Sharing: Consequences for Peer Adoption
"Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt.
"We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing.
"We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly."
to:NB  social_influence  social_media  experimental_economics  re:homophily_and_confounding  bakshy.eytan 
november 2013 by cshalizi
Choose what you like or like what you choose? Identifying Influence and Homophily out of Individual Decisions
"We investigate the microfoundations of the identification problem related to social influence and homophily. Focusing on the individual decision making of interacting individuals, we investigate how they affect each other’s behaviors. We propose simple and direct measures of homophily and influence by making use of individual preferences of these interacting individuals. Since in many occasions, preferences are not easily observed, we extend our analysis to the observables, decision outcomes. In order to infer the underlying preferences of interacting individuals out of their decision outcomes, we follow a foundational approach. We analyze the behavioral characteristics of individual de- cision making that includes interaction and finally we make use of
the tools that are provided by revealed preference theory in order to uncover the underlying preferences of the individuals. Based on re- vealed preference analysis, we revisit our measurement techniques for homophily and influence."

- Gets only partial identification.
to:NB  to_read  decision_theory  social_influence  re:homophily_and_confounding 
november 2013 by cshalizi
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes | AISTATS 2013 | JMLR W&CP
"How will the behaviors of individuals in a social network be influenced by their neighbors, the authorities and the communities? Such knowledge is often hidden from us and we only observe its manifestation in the form of recurrent and time-stamped events occurring at the individuals involved. It is an important yet challenging problem to infer the network of social inference based on the temporal patterns of these historical events. We propose a convex optimization approach to discover the hidden network of social influence by modeling the recurrent events at different individuals as multi-dimensional Hawkes processes. Furthermore, our estimation procedure, using nuclear and ℓ1 norm regularization simultaneously on the parameters, is able to take into account the prior knowledge of the presence of neighbor interaction, authority influence, and community coordination. To efficiently solve the problem, we also design an algorithm ADM4 which combines techniques of alternating direction method of multipliers and majorization minimization. We experimented with both synthetic and real world data sets, and showed that the proposed method can discover the hidden network more accurately and produce a better predictive model."
in_NB  optimization  point_processes  social_influence  statistics  re:homophily_and_confounding 
september 2013 by cshalizi
Executive Networks and Firm Policies: Evidence from the Random Assignment of MBA Peers
"Using the historical random assignment of MBA students to sections at Harvard Business School (HBS), I explore how executive peer networks can affect managerial decision making. Within an HBS class, firm outcomes are significantly more similar among graduates from the same section than among graduates from different sections, with the strongest effects in executive compensation and acquisitions strategy. I demonstrate the role of ongoing social interactions by showing that peer effects are more than twice as strong in the year following staggered alumni reunions. Supplementary tests suggest that peer influence can operate in ways that do not contribute to firm productivity."
to:NB  social_networks  corporations  economics  institutions  social_influence  re:homophily_and_confounding  to_read  entableted 
september 2013 by cshalizi
[1308.5015] The Simple Rules of Social Contagion
"It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is significantly more complex than the prediction of the pathogen model. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of the exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We apply our model to real-time forecasting of user behavior."
to:NB  to_read  social_influence  contagion  networked_life  social_media  re:homophily_and_confounding  lerman.kristina 
september 2013 by cshalizi
Social Networks and Popular Understanding of Science and Health Sharing Disparities Brian G. Southwell
"Using social media and peer-to-peer networks to teach people about science and health may seem like an obvious strategy. Yet recent research suggests that systematic reliance on social networks may be a recipe for inequity. People are not consistently inclined to share information with others around them, and many people are constrained by factors outside of their immediate control. Ironically, the highly social nature of humankind complicates the extent to which we can live in a society united solely by electronic media.
"Stretching well beyond social media, this book documents disparate tendencies in the ways people learn and share information about health and science. By reviewing a wide array of existing research—ranging from a survey of New Orleans residents in the weeks after Hurricane Katrina to analysis of Twitter posts related to H1N1 to a physician-led communication campaign explaining the benefits of vaginal birth—Brian Southwell explains why some types of information are more likely to be shared than others and how some people never get exposed to seemingly widely available information."
to:NB  books:noted  social_networks  social_media  social_influence  epidemiology_of_ideas  re:homophily_and_confounding  sociology 
august 2013 by cshalizi
[1307.7142] Temporal influence over the Last.fm social network
"Several recent results show the influence of social contacts to spread certain properties over the network, but others question the methodology of these experiments by proposing that the measured effects may be due to homophily or a shared environment. In this paper we justify the existence of the social influence by considering the temporal behavior of Last.fm users. In order to clearly distinguish between friends sharing the same interest, especially since Last.fm recommends friends based on similarity of taste, we separated the timeless effect of similar taste from the temporal impulses of immediately listening to the same artist after a friend. We measured strong increase of listening to a completely new artist in a few hours period after a friend compared to non-friends representing a simple trend or external influence. In our experiment to eliminate network independent elements of taste, we improved collaborative filtering and trend based methods by blending with simple time aware recommendations based on the influence of friends. Our experiments are carried over the two-year "scrobble" history of 70,000 Last.fm users."

- Does not address the confounding problem at all (despite the abstract).
to:NB  homophily  social_media  social_influence  re:homophily_and_confounding  shot_after_a_fair_trial 
july 2013 by cshalizi
Selection effects in online sharing
"Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt.
"We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly."
to:NB  experimental_sociology  social_influence  social_networks  re:homophily_and_confounding  bakshy.eytan 
june 2013 by cshalizi
[1305.6156] Estimating Average Causal Effects Under Interference Between Units
"This paper presents a randomization-based framework for estimating causal effects under interference between units. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. We illustrate and assess empirical performance with a naturalistic simulation using network data from American high schools. We discuss other applications and sketch approaches for situations where there is uncertainty about the form of interference."
to:NB  to_read  heard_the_talk  experimental_design  statistics  social_networks  re:homophily_and_confounding  samii.cyrus  aronow.peter 
may 2013 by cshalizi
[1305.5235] Lognormal Infection Times of Online Information Spread
"The infection times of individuals in online information spread such as the inter-arrival time of Twitter messages or the propagation time of news stories on a social media site can be explained through a convolution of lognormally distributed observation and reaction times of the individual participants. Experimental measurements support the lognormal shape of the individual contributing processes, and have resemblance to previously reported lognormal distributions of human behavior and contagious processes."
to:NB  heavy_tails  social_media  contagion  re:homophily_and_confounding  have_read 
may 2013 by cshalizi
Biased assimilation, homophily, and the dynamics of polarization
"We study the issue of polarization in society through a model of opinion formation. We say an opinion formation process is polarizing if it results in increased divergence of opinions. Empirical studies have shown that homophily, i.e., greater interaction between like-minded individuals, results in polarization. However, we show that DeGroot’s well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot’s model to account for a phenomenon well known in social psychology as biased assimilation: When presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position, thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in polarization if individuals are sufficiently biased. In other words, homophily alone, without biased assimilation, is not sufficient to polarize society. Quite interestingly, biased assimilation also provides a framework to analyze the polarizing effect of Internet-based recommender systems that show us personalized content."
to:NB  social_networks  social_influence  re:homophily_and_confounding  homophily 
may 2013 by cshalizi
"We used network-based diffusion analysis to reveal the cultural spread of a naturally occurring foraging innovation, lobtail feeding, through a population of humpback whales (Megaptera novaeangliae) over a period of 27 years. Support for models with a social transmission component was 6 to 23 orders of magnitude greater than for models without. The spatial and temporal distribution of sand lance, a prey species, was also important in predicting the rate of acquisition. Our results, coupled with existing knowledge about song traditions, show that this species can maintain multiple independently evolving traditions in its populations. These insights strengthen the case that cetaceans represent a peak in the evolution of nonhuman culture, independent of the primate lineage."
to:NB  to_read  whales  social_influence  diffusion_of_innovations  social_networks  re:homophily_and_confounding  cultural_transmission 
april 2013 by cshalizi
Graphical Causal Models - Springer
"This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, d-separation, confounding, endogenous selection, and overcontrol. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than an associational concept, that conventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, time-point treatments (including the famous backdoor criterion), as well identification criteria for multiple, time-varying treatments."
to:NB  graphical_models  causal_inference  statistics  elwert.felix  social_science_methodology  re:homophily_and_confounding 
april 2013 by cshalizi
Social Networks and Causal Inference - Springer
"This chapter reviews theoretical developments and empirical studies related to causal inference on social networks from both experimental and observational studies. Discussion is given to the effect of experimental interventions on outcomes and behaviors and how these effects relate to the presence of social ties, the position of individuals within the network, and the underlying structure and properties of the network. The effects of such experimental interventions on changing the network structure itself and potential feedback between behaviors and network changes are also discussed. With observational data, correlations in behavior or outcomes between individuals with network ties may be due to social influence, homophily, or environmental confounding. With cross-sectional data these three sources of correlation cannot be distinguished. Methods employing longitudinal observational data that can help distinguish between social influence, homophily, and environmental confounding are described, along with their limitations. Proposals are made regarding future research directions and methodological developments that would help put causal inference on social networks on a firmer theoretical footing."
to:NB  to_read  vanderweele.tyler  causal_inference  statistics  social_networks  network_data_analysis  social_influence  re:homophily_and_confounding 
april 2013 by cshalizi
Estimating Average Causal Effects Under General Interference
"This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. Conservative estimators of the randomization variance of the average treatment effects estimators are presented, as is justification for confidence intervals based on a normal approximation. Examples rele- vant to research in environmental protection, networks experiments, “viral marketing,” two-stage disease prophylaxis trials, and stepped-wedge designs are presented."
to:NB  to_read  experimental_design  causal_inference  social_networks  network_data_analysis  social_influence  re:homophily_and_confounding 
april 2013 by cshalizi
Phys. Rev. E 87, 032805 (2013): Contagion dynamics in time-varying metapopulation networks
"The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose links represent the migration routes among them. The connections have been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold being even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks."
in_NB  epidemic_models  networks  re:do-institutions-evolve  re:homophily_and_confounding  social_influence 
march 2013 by cshalizi
[1302.2472] Quantifying the effects of social influence
"How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to repeatedly guess the correct answer to factual questions, while having only aggregated information about the answers of others. While the response of humans to aggregated information is a widely observed phenomenon, it has not been investigated quantitatively, in a controlled setting. We found that the adjustment of individual guesses depends linearly on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple, statistical regularity. It holds across all questions analysed, even though the correct answers differ in several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. It also complements previous results on the nonlinear response in information-rich scenarios. We argue that the nature of the response to social influence crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence."
to:NB  social_influence  social_psychology  experimental_sociology  re:homophily_and_confounding 
march 2013 by cshalizi
Ioannides, Y.M.: From Neighborhoods to Nations: The Economics of Social Interactions.
"Just as we learn from, influence, and are influenced by others, our social interactions drive economic growth in cities, regions, and nations--determining where households live, how children learn, and what cities and firms produce. From Neighborhoods to Nations synthesizes the recent economics of social interactions for anyone seeking to understand the contributions of this important area. Integrating theory and empirics, Yannis Ioannides explores theoretical and empirical tools that economists use to investigate social interactions, and he shows how a familiarity with these tools is essential for interpreting findings. The book makes work in the economics of social interactions accessible to other social scientists, including sociologists, political scientists, and urban planning and policy researchers.
"Focusing on individual and household location decisions in the presence of interactions, Ioannides shows how research on cities and neighborhoods can explain communities' composition and spatial form, as well as changes in productivity, industrial specialization, urban expansion, and national growth. The author examines how researchers address the challenge of separating personal, social, and cultural forces from economic ones. Ioannides provides a toolkit for the next generation of inquiry, and he argues that quantifying the impact of social interactions in specific contexts is essential for grasping their scope and use in informing policy.
"Revealing how empirical work on social interactions enriches our understanding of cities as engines of innovation and economic growth, From Neighborhoods to Nations carries ramifications throughout the social sciences and beyond."
to:NB  social_networks  econometrics  statistics  social_influence  economics  re:homophily_and_confounding 
december 2012 by cshalizi
Streams of Thought: How Ties Form and Influence Flows among New Faculty
"Research universities are often depicted as anomic organizations, due to high levels of individual and
departmental autonomy combined with the specialization of academic knowledge. We test the alternate view that autonomy and specialization lead to social cohesion when institutions foster meaningful contact among individuals with sequentially overlapping distributions of intellectual specialization. Drawing from comprehensive longitudinal data on publications and social networks among faculty members at a leading university, we use dynamic network models to assess when intellectual ties form and influence flows among a panel of 216 faculty members. Findings show that overlapping and complimentary ideas give rise to stronger ties (co-authorships) that spread through transitive closure, and weaker ties (joint dissertation committees without coauthorships) that expand as non-transitive chains. Once in place, these ties form a largely integrated network that acts as a conduit for spreading intellectual influence, contingent upon a combination of tie strength, knowledge domain, and, in the case of the social sciences, the average status (network centrality) of one’s contacts. This provides a fluid and reciprocally causal view of ideas and networks, confirming how micro-dynamics generate sequentially overlapping macro- structures and clarifying implications for how influence flows."

- On a first and admittedly superficial scan, hopelessly confounded. (Or rather, confounding is evaded by _declaring_ that all relevant variables are measured, and that nothing measured is itself a collider. This is a very convenient identification strategy...) Last tag applies.
in_NB  social_life_of_the_mind  social_influence  academia  social_networks  re:homophily_and_confounding  to_be_shot_after_a_fair_trial  to_read 
november 2012 by cshalizi
Causal Inference for Networks
"Suppose that we observe a population of causally connected units according to a network. On each unit we observe a set of potentially connected units that contains the true connections, and a longitudinal data structure, which includes time-dependent exposure or treatment, time-dependent covariates, a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of the unit specific outcomes under different stochastic interventions one wishes to evaluate. By varying the network structure, this covers a large range of estimation problems ranging from independent units, independent clusters of units, anda single cluster of units in which each unit has a limited number of connections to other units. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation."

--- I am interested to see what he does with the homophily issue. Van der Laan is a serious researcher, though that often leads to huge, monographic papers like this one...
in_NB  to_read  causal_inference  statistics  network_data_analysis  social_influence  van_der_laan.mark  via:nyhan  re:homophily_and_confounding 
october 2012 by cshalizi
How social and genetic factors predict friendship networks
"Recent research suggests that the genotype of one individual in a friendship pair is predictive of the genotype of his/her friend. These results provide tentative support for the genetic homophily perspective, which has important implications for social and genetic epidemiology because it substantiates a particular form of gene–environment correlation. This process may also have important implications for social scientists who study the social factors related to health and health-related behaviors. We extend this work by considering the ways in which school context shapes genetically similar friendships. Using the network, school, and genetic information from the National Longitudinal Study of Adolescent Health, we show that genetic homophily for the TaqI A polymorphism within the DRD2 gene is stronger in schools with greater levels of inequality. Our results suggest that individuals with similar genotypes may not actively select into friendships; rather, they may be placed into these contexts by institutional mechanisms outside of their control. Our work highlights the fundamental role played by broad social structures in the extent to which genetic factors explain complex behaviors, such as friendships."

- The correction, PNAS 109 (2012): 21551 (doi:10.1073/pnas.1220043110) only concerns funding / credit / access to the original AddHealth data.
to_read  social_networks  human_genetics  institutions  sociology  homophily  re:homophily_and_confounding  re:g_paper  in_NB 
october 2012 by cshalizi
A Cluster-based Method for Isolating Influence on Twitter
"This paper demonstrates a cluster-based method to isolate influence in social network- based observational data, where "influence" is defined to mean that one person posts about a topic online and a second person posts about the same topic because he or she read the first post. Isolating influence in observational data is difficult, because we may observe that connected people discuss the same topic in proximate periods for reasons other than influence, in- cluding homophily–connected people are similar–and exogenous shock; they may have learned of the topic from some external source. We employ a matched sample estimation technique that has been used in the past to measure influence by controlling for demographic and usage based homophily, and add to the matching scheme a cluster ID. Our contribution is two-fold: First, we provide preliminary evidence that social network-based clusters capture homophily, indicating that a network-based attribute approach may not only capture homophily but also may be used in lieu of using demographic attributes for matching similar users in scenarios when privacy preservation is a concern. Second, we show that by adding a network position attribute, a cluster ID, when matching similar users, we can isolate influence better. We believe that our approach to isolate influence can have a broad impact on problems where social networks and associated behaviors can be observed over time."
in_NB  social_networks  community_discovery  social_influence  homophily  re:homophily_and_confounding  social_media 
september 2012 by cshalizi
Dyadic Information Transfer: A Network Study of Organizational Information Spread
"Previous research on diffusion networks has focused predominately on the final step in diffusion, adoption, rather than the primary phase of information acquisition. To explain the information spread, I propose a theory of information transfer based on the characteristics of the dyad and the characteristics of the sender. To understand information transfer, I examine the transmission of project information within a large corporation. I posit that diffusion patterns differ based on the relational embeddedness and characteristics of the sender. In an empirical analysis of the transfer of information shared between individuals within Enron between 1998-2003, I find that the probability that information will be transferred between sender and recipient is influenced by several factors: (1) the attributes of the relationship between the sender and recipient, (2) social structure surrounding the sender and the recipient, (3) and previous sharing behavior of the sender."
to:NB  to_read  social_networks  diffusion_of_innovations  re:homophily_and_confounding 
august 2012 by cshalizi
Reasoning about Interference Between Units
"If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize let alone execute. In this paper, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the “no interference” assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applica- ble to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena."
in_NB  to_read  social_networks  networks  causal_inference  statistics  hypothesis_testing  re:homophily_and_confounding 
july 2012 by cshalizi
[1112.1010] Twitter reciprocal reply networks exhibit assortativity with respect to happiness
"The advent of social media has provided an extraordinary, if imperfect, 'big data' window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users' average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar's number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions."

Published version reprint: http://www.uvm.edu/~cdanfort/research/bliss-jocs-2012.pdf
to:NB  social_networks  social_media  re:homophily_and_confounding  dodds.peter_sheridan  via:nyhan 
july 2012 by cshalizi
Identifying Influential and Susceptible Members of Social Networks
"Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible to influence than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product offered. Analysis of influence and susceptibility together with network structure revealed that influential individuals are less susceptible to influence than noninfluential individuals and that they cluster in the network while susceptible individuals do not, which suggests that influential people with influential friends may be instrumental in the spread of this product in the network."
to:NB  social_networks  social_influence  experimental_sociology  aral.sinan  have_read  re:homophily_and_confounding 
july 2012 by cshalizi
PLoS ONE: An Actor-Based Model of Social Network Influence on Adolescent Body Size, Screen Time, and Playing Sports
"Recent studies suggest that obesity may be “contagious” between individuals in social networks. Social contagion (influence), however, may not be identifiable using traditional statistical approaches because they cannot distinguish contagion from homophily (the propensity for individuals to select friends who are similar to themselves) or from shared environmental influences. In this paper, we apply the stochastic actor-based model (SABM) framework developed by Snijders and colleagues to data on adolescent body mass index (BMI), screen time, and playing active sports. Our primary hypothesis was that social influences on adolescent body size and related behaviors are independent of friend selection. Employing the SABM, we simultaneously modeled network dynamics (friendship selection based on homophily and structural characteristics of the network) and social influence. We focused on the 2 largest schools in the National Longitudinal Study of Adolescent Health (Add Health) and held the school environment constant by examining the 2 school networks separately (N = 624 and 1151). Results show support in both schools for homophily on BMI, but also for social influence on BMI. There was no evidence of homophily on screen time in either school, while only one of the schools showed homophily on playing active sports. There was, however, evidence of social influence on screen time in one of the schools, and playing active sports in both schools. These results suggest that both homophily and social influence are important in understanding patterns of adolescent obesity. Intervention efforts should take into consideration peers’ influence on one another, rather than treating “high risk” adolescents in isolation."

For "may not be identifiable", read "is not identifiable". From a casual scan, I don't see how they get around this, other than by _positing_ that they're measuring all the variables relevant to the choice of friends, which doesn't seem to be checked. Last tag applies.
to:NB  to_read  social_networks  homophily  social_influence  contagion  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
july 2012 by cshalizi
Network Interventions
"The term “network interventions” describes the process of using social network data to accelerate behavior change or improve organizational performance. In this Review, four strategies for network interventions are described, each of which has multiple tactical alternatives. Many of these tactics can incorporate different mathematical algorithms. Consequently, researchers have many intervention choices at their disposal. Selecting the appropriate network intervention depends on the availability and character of network data, perceived characteristics of the behavior, its existing prevalence, and the social context of the program."

- Does it even mention the critiques of Christakis & Fowler? Seems unlikely, since they (apparently) took their model from Valente.
to:NB  to_read  to_be_shot_after_a_fair_trial  social_networks  experimental_sociology  social_influence  re:homophily_and_confounding  re:critique_of_diffusion  experimental_design  re:do_not_adjust_your_receiver  have_skimmed 
july 2012 by cshalizi
Estimating the Causal Effects of Social Interaction with Endogenous Networks
"Identifying causal effects attributable to network membership is a key challenge in empirical studies of social networks. In this article, we examine the consequences of endogeneity for inferences about the effects of networks on network members’ behavior. Using the House office lottery (in which newly elected members select their office spaces in a randomly chosen order) as an instrumental variable to estimate the causal impact of legislative networks on roll call behavior and cosponsorship decisions in the 105th–112th Houses, we find no evidence that office proximity affects patterns of legislative behavior. These results contrast with decades of congressional scholarship and recent empirical studies. Our analysis demonstrates the importance of accounting for selection processes and omitted variables in estimating the causal impact of networks."
in_NB  causal_inference  re:critique_of_diffusion  social_influence  congress  network_data_analysis  social_networks  homophily  re:homophily_and_confounding 
may 2012 by cshalizi
[0809.5032] Identifiability of parameters in latent structure models with many observed variables
"While hidden class models of various types arise in many statistical applications, it is often difficult to establish the identifiability of their parameters. Focusing on models in which there is some structure of independence of some of the observed variables conditioned on hidden ones, we demonstrate a general approach for establishing identifiability utilizing algebraic arguments. A theorem of J. Kruskal for a simple latent-class model with finite state space lies at the core of our results, though we apply it to a diverse set of models. These include mixtures of both finite and nonparametric product distributions, hidden Markov models and random graph mixture models, and lead to a number of new results and improvements to old ones. In the parametric setting, this approach indicates that for such models, the classical definition of identifiability is typically too strong. Instead generic identifiability holds, which implies that the set of nonidentifiable parameters has measure zero, so that parameter inference is still meaningful. In particular, this sheds light on the properties of finite mixtures of Bernoulli products, which have been used for decades despite being known to have nonidentifiable parameters. In the nonparametric setting, we again obtain identifiability only when certain restrictions are placed on the distributions that are mixed, but we explicitly describe the conditions."
in_NB  statistics  identifiability  mixture_models  inference_to_latent_objects  re:homophily_and_confounding  to_read 
february 2012 by cshalizi
Social Influence, Binary Decisions and Collective Dynamics
"In this paper we address the general question of how social influence determines collective outcomes for large populations of individuals faced with binary decisions. First, we define conditions under which the behavior of individuals making binary decisions can be described in terms of what we call an influence-response function: a one-dimensional function of the (weighted) number of individuals choosing each of the alternatives. And second, we demonstrate that, under the assumptions of global and anonymous interactions, general knowledge of the influence-response functions is sufficient to compute equilibrium, and even non-equilibrium, properties of the collective dynamics. By enabling us to treat in a consistent manner classes of decisions that have previously been analyzed separately, our framework allows us to find similarities between apparently quite different kinds of decision situations, and conversely to identify important differences between decisions that would otherwise appear very similar."
to:NB  to_read  re:do-institutions-evolve  re:homophily_and_confounding  social_life_of_the_mind  social_influence  herding  watts.duncan  kith_and_kin 
january 2012 by cshalizi
Social selection and peer influence in an online social network
"Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes."

It will be interested to see how they argue this isn't confounded six ways from Sunday.
in_NB  to_read  re:homophily_and_confounding  social_networks  social_influence  homophily  social_media  to_be_shot_after_a_fair_trial 
december 2011 by cshalizi
An Experimental Study of Homophily in the Adoption of Health Behavior
"How does the composition of a population affect the adoption of health behaviors and innovations? Homophily—similarity of social contacts—can increase dyadic-level influence, but it can also force less healthy individuals to interact primarily with one another, thereby excluding them from interactions with healthier, more influential, early adopters. As a result, an important network-level effect of homophily is that the people who are most in need of a health innovation may be among the least likely to adopt it. Despite the importance of this thesis, confounding factors in observational data have made it difficult to test empirically. We report results from a controlled experimental study on the spread of a health innovation through fixed social networks in which the level of homophily was independently varied. We found that homophily significantly increased overall adoption of a new health behavior, especially among those most in need of it."
in_NB  to_read  social_networks  experimental_sociology  re:homophily_and_confounding  homophily  diffusion_of_innovations  contagion  social_influence 
december 2011 by cshalizi
[1111.0073] Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily
We study how a behavior (an idea, buying a product, having a disease, adopting a cultural fad or a technology) spreads among agents in an a social network that exhibits segregation or homophily (the tendency of agents to associate with others similar to themselves). Individuals are distinguished by their types (e.g., race, gender, age, wealth, religion, profession, etc.) which, together with biased interaction patterns, induce heterogeneous rates of adoption. We identify the conditions under which a behavior diffuses and becomes persistent in the population. These conditions relate to the level of homophily in a society, the underlying proclivities of various types for adoption or infection, as well as how each type interacts with its own type. In particular, we show that homophily can facilitate diffusion from a small initial seed of adopters.
to:NB  to_read  diffusion_of_innovations  contagion  homophily  re:homophily_and_confounding  jackson.matthew_o.  re:do-institutions-evolve 
november 2011 by cshalizi
[1110.0535] Modeling the adoption of innovations in the presence of geographic and media influences
"While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves."
diffusion_of_innovations  social_influence  twitter  social_media  re:homophily_and_confounding  to:NB 
october 2011 by cshalizi
[1108.2228] A consistent dot product embedding for stochastic blockmodel graphs
"We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedded vectors are clustered through minimization of a mean square error/criteria. We prove that this method is consistent for assigning nodes to blocks, as only a negligible number of nodes will be mis-assigned. We prove consistency of the method for directed and undirected graphs. The consistent block assignment makes possible consistent parameter estimation for a stochastic blockmodel. We extend the result for when the number of blocks grows slowly with the number of nodes. Our method is also computationally feasible even for very large graphs."
community_discovery  network_data_analysis  in_NB  statistics  re:homophily_and_confounding 
august 2011 by cshalizi
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