cshalizi + to_teach:undergrad-ada   437

[1909.06539] Not again! Data Leakage in Digital Pathology
"Bioinformatics of high throughput omics data (e.g. microarrays and proteomics) has been plagued by uncountable issues with reproducibility at the start of the century. Concerns have motivated international initiatives such as the FDA's led MAQC Consortium, addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs). For instance, repreated cross-validation is a standard procedure meant at mitigating the risk that information from held-out validation data may be used during model selection. We prove here that, many years later, Data Leakage can still be a non-negligible overfitting source in deep learning models for digital pathology. In particular, we evaluate the impact of (i) the presence of multiple images for each subject in histology collections; (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject. We verify that accuracy scores may be inflated up to 41%, even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits. Results are replicated for 4 classification tasks in digital pathology on 3 datasets, for a total of 373 subjects, and 543 total slides (around 27, 000 tiles). Impact of applying transfer learning strategies with models pre-trained on general-purpose or digital pathology datasets is also discussed."
to:NB  cross-validation  statistics  bad_data_analysis  to_teach:undergrad-ADA  to_teach:data-mining 
10 weeks ago by cshalizi
[1901.01241] Nonparametric Instrumental Variables Estimation Under Misspecification
"We show that nonparametric instrumental variables estimators are highly sensitive to misspecification: an arbitrarily small deviation from instrumental validity can lead to large asymptotic bias for a broad class of estimators. The problem is mitigated if strong restrictions on the structural function are imposed in estimation. However, if the true function does not obey the restrictions, then imposing them imparts bias. Therefore, there is a trade-off between the sensitivity to invalid instruments and bias from imposing excessive restrictions. We propose a partial identification approach that allows a researcher to explicitly and transparently examine this trade-off and make inferences about the structural function that are valid under a small failure of instrumental validity. We construct a simple, consistent estimator of the identified set. We apply our methods to the empirical setting of Blundell et al. (2007) and Horowitz (2011) to estimate shape-invariant Engel curves."
to:NB  instrumental_variables  causal_inference  nonparametrics  statistics  to_teach:undergrad-ADA 
august 2019 by cshalizi
Bootstrap Methods in Econometrics | Annual Review of Economics
"The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited."
to:NB  bootstrap  statistics  economics  to_teach:undergrad-ADA  horowitz.joel 
august 2019 by cshalizi
Evaluating Probabilistic Forecasts with scoringRules | Jordan | Journal of Statistical Software
"Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature."
to:NB  prediction  statistics  to_teach:undergrad-ADA  to_teach:data-mining 
august 2019 by cshalizi
Free trade and opioid overdose death in the United States - ScienceDirect
"Opioid overdose deaths in the U.S. rose dramatically after 1999, but also exhibited substantial geographic variation. This has largely been explained by differential availability of prescription and non-prescription opioids, including heroin and fentanyl. Recent studies explore the underlying role of socioeconomic factors, but overlook the influence of job loss due to international trade, an economic phenomenon that disproportionately harms the same regions and demographic groups at the heart of the opioid epidemic. We used OLS regression and county-year level data from the Centers for Disease Controls and the Department of Labor to test the association between trade-related job loss and opioid-related overdose death between 1999 and 2015. We find that the loss of 1000 trade-related jobs was associated with a 2.7 percent increase in opioid-related deaths. When fentanyl was present in the heroin supply, the same number of job losses was associated with a 11.3 percent increase in opioid-related deaths."

--- I'm very skeptical about OLS here. Something like nearest neighbors would be better here, but I'm not sure how to handle spatial correlation.
to:NB  to_read  drugs  whats_gone_wrong_with_america  class_struggles_in_america  econometrics  statistics  globalization  to_teach:data_over_space_and_time  to_teach:undergrad-ADA  causal_inference 
july 2019 by cshalizi
Scalable Visualization Methods for Modern Generalized Additive Models: Journal of Computational and Graphical Statistics: Vol 0, No 0
"In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online."
to:NB  additive_models  visual_display_of_quantitative_information  computational_statistics  statistics  R  to_teach:undergrad-ADA 
july 2019 by cshalizi
Life after Lead: Effects of Early Interventions for Children Exposed to Lead
"Lead pollution is consistently linked to cognitive and behavioral impairments, yet little is known about the benefits of public health interventions for children exposed to lead. This paper estimates the long-term impacts of early-life interventions (e.g. lead remediation, nutritional assessment, medical evaluation, developmental surveillance, and public assistance referrals) recommended for lead-poisoned children. Using linked administrative data from Charlotte, NC, we compare outcomes for children who are similar across observable characteristics but differ in eligibility for intervention due to blood lead test results. We find that the negative outcomes previously associated with early-life exposure can largely be reversed by intervention."

--- The last tag, as usual, is conditional on liking the paper after reading it, and on replication data being available.
to:NB  to_read  lead  cognitive_development  sociology  causal_inference  to_teach:undergrad-ADA 
june 2019 by cshalizi
Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models | Annual Review of Sociology
"Methods textbooks in sociology and other social sciences routinely recommend the use of the logit or probit model when an outcome variable is binary, an ordered logit or ordered probit when it is ordinal, and a multinomial logit when it has more than two categories. But these methodological guidelines take little or no account of a body of work that, over the past 30 years, has pointed to problematic aspects of these nonlinear probability models and, particularly, to difficulties in interpreting their parameters. In this review, we draw on that literature to explain the problems, show how they manifest themselves in research, discuss the strengths and weaknesses of alternatives that have been suggested, and point to lines of further analysis."
to:NB  statistics  classifiers  bad_data_analysis  to_teach:undergrad-ADA 
may 2019 by cshalizi
estimation - Variance of a sample covariance for normal variables - Cross Validated
To make into an exercise. (As one of the answers points out, there is nothing here which turns on using a Gaussian distribution.)
probability  statistics  to_teach:undergrad-ADA  to_teach:linear_models  to_teach:data_over_space_and_time 
may 2019 by cshalizi
[1904.02438] Cross-Validation for Correlated Data
"K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional data assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-i.i.d data. This paper analyzes CV for correlated data. We present a criterion for suitability of CV, and introduce a bias corrected cross-validation prediction error estimator, CVc, which is suitable in many settings involving correlated data, where CV is invalid. Our theoretical results are also demonstrated numerically."
to:NB  statistics  cross-validation  time_series  rosset.saharon  to_teach:undergrad-ADA  to_teach:data_over_space_and_time 
april 2019 by cshalizi
The Bias Is Built In: How Administrative Records Mask Racially Biased Policing by Dean Knox, Will Lowe, Jonathan Mummolo :: SSRN
"Researchers often lack the necessary data to credibly estimate racial bias in policing. In particular, police administrative records lack information on civilians that police observe but do not investigate. In this paper, we show that if police racially discriminate when choosing whom to investigate, using administrative records to estimate racial bias in police behavior amounts to post-treatment conditioning, and renders many quantities of interest unidentified---even among investigated individuals---absent strong and untestable assumptions. In most cases, no set of controls can eliminate this statistical bias, the exact form of which we derive through principal stratification in a causal mediation framework. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show traditional estimation techniques can severely underestimate levels of racially biased policing or even mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare."
to:NB  to_read  causal_inference  police  discrimination  statistics  to_teach:undergrad-ADA  via:henry_farrell 
february 2019 by cshalizi
Recognising when you don’t know - Biased and Inefficient
(Some nice shade is thrown on the difference between machine learning and statistics --- excuse me, "data science".)
classifiers  mushrooms  statistics  to_teach:data-mining  to_teach:undergrad-ADA  lumley.thomas 
february 2019 by cshalizi
The Taxing Deed of Globalization
"This paper examines the effects of globalization on the distribution of worker-specific labor taxes using a unique set of tax calculators. We find a differential effect of higher trade and factor mobility on relative tax burdens in 1980–1993 versus 1994–2007 in the OECD. Prior to 1994, greater openness meant that higher income earners were taxed progressively more. However, after 1994, we document a globalization-induced rise in the labor income tax burden of the middle class, while the top 1 percent of workers and employees faced a reduction in their tax burden of 0.59–1.45 percentage points."
to:NB  globalization  economics  to_teach:undergrad-ADA 
january 2019 by cshalizi
The association between adolescent well-being and digital technology use | Nature Human Behaviour
"The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change."

--- This sounds awesome, but will need to be read carefully.
to:NB  to_read  networked_life  sociology  statistics  model_checking  to_teach:undergrad-ADA  to_be_shot_after_a_fair_trial  re:actually-dr-internet-is-the-name-of-the-monsters-creator 
january 2019 by cshalizi
Youth-Parent Socialization Panel Study, 1965-1997: Four Waves Combined
"The Youth-Parent Socialization Panel Study is a series of surveys designed to assess political continuity and change across time for biologically-related generations and to gauge the impact of life-stage events and historical trends on the behaviors and attitudes of respondents. A national sample of high school seniors and their parents was first surveyed in 1965. Subsequent surveys of the same individuals were conducted in 1973, 1982, and 1997. This data collection combines all four waves of youth data for the study. The general objective of the data collection was to study the dynamics of political attitudes and behaviors by obtaining data on the same individuals as they aged from approximately 18 years of age in 1965 to 50 years of age in 1997. Especially when combined with other elements of the study as released in other ICPSR collections in the Youth Studies Series, this data collection facilitates the analysis of generational, life cycle, and historical effects and political influences on relationships within the family. This data collection also has several distinctive properties. First, it is a longitudinal study of a particular cohort, a national sample from the graduating high school class of 1965. Second, it captures the respondents at key points in their life stages -- at ages 18, 26, 35, and 50. Third, the dataset contains many replicated measures over time as well as some measures unique to each data point. Fourth, there is detailed information about the respondents' life histories. Background variables include age, sex, religious orientation, level of religious participation, marital status, ethnicity, educational status and background, place of residence, family income, and employment status."

--- Used in Rochon's book about value change, in a way which would make it a good case study for propensity-score matching (which Rochon did _not_ do, confounding his inferences). Query, can I get access via CMU, or are we not part of the consortium?
data_sets  us_politics  public_opinion  to_teach:undergrad-ADA 
january 2019 by cshalizi
Robots at Work | The Review of Economics and Statistics | MIT Press Journals
"We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in seventeen countries from 1993 to 2007 and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.36 percentage points to annual labor productivity growth, while at the same time raising total factor productivity and lowering output prices. Our estimates also suggest that robots did not significantly reduce total employment, although they did reduce low-skilled workers’ employment share."

- Last tag for the instrumental variables (if they look sensible and perhaps especially if they do not)
to:NB  economics  instrumental_variables  robots_and_robotics  to_teach:undergrad-ADA 
january 2019 by cshalizi
Confidence intervals for GLMs
For the trick about finding the inverse link function.
regression  R  to_teach:undergrad-ADA  via:kjhealy 
december 2018 by cshalizi
The Effect of Media Coverage on Mass Shootings | IZA - Institute of Labor Economics
"Can media coverage of shooters encourage future mass shootings? We explore the link between the day-to-day prime time television news coverage of shootings on ABC World News Tonight and subsequent mass shootings in the US from January 1, 2013 to June 23, 2016. To circumvent latent endogeneity concerns, we employ an instrumental variable strategy: worldwide disaster deaths provide an exogenous variation that systematically crowds out shooting-related coverage. Our findings consistently suggest a positive and statistically significant effect of coverage on the number of subsequent shootings, lasting for 4-10 days. At its mean, news coverage is suggested to cause approximately three mass shootings in the following week, which would explain 55 percent of all mass shootings in our sample. Results are qualitatively consistent when using (i) additional keywords to capture shooting-related news coverage, (ii) alternative definitions of mass shootings, (iii) the number of injured or killed people as the dependent variable, and (iv) an alternative, longer data source for mass shootings from 2006-2016."
to:NB  to_read  contagion  causal_inference  to_teach:undergrad-ADA  to_be_shot_after_a_fair_trial  previous_tag_was_in_poor_taste 
december 2018 by cshalizi
How to forecast an American’s vote - All politics is identity politics
This looks like a nice case-study for when I teach logistic regression in the spring, provided there's replication data. It'd be even better if there was a follow-up on how well this actually predicted!
track_down_references  logistic_regression  us_politics  to_teach:undergrad-ADA 
november 2018 by cshalizi
General Resampling Infrastructure • rsample
"rsample contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:
"traditional resampling techniques for estimating the sampling distribution of a statistic and
"estimating model performance using a holdout set
"The scope of rsample is to provide the basic building blocks for creating and analyzing resamples of a data set but does not include code for modeling or calculating statistics. The “Working with Resample Sets” vignette gives demonstrations of how rsample tools can be used."
to:NB  R  computational_statistics  to_teach:statcomp  to_teach:undergrad-ADA  via:? 
august 2018 by cshalizi
[1706.08576] Invariant Causal Prediction for Nonlinear Models
"An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system's underlying causal structure. To this end, 'invariant causal prediction' (ICP) (Peters et al., 2016) has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straight-forward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence. In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure "Invariant residual distribution test". In general, we observe that the performance of all approaches is critically dependent on the true (unknown) causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables. As a real-world example, we consider fertility rate modelling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates."
to:NB  causal_inference  causal_discovery  statistics  regression  prediction  peters.jonas  meinshausen.nicolai  to_read  heard_the_talk  to_teach:undergrad-ADA  re:ADAfaEPoV 
may 2018 by cshalizi
[1501.01332] Causal inference using invariant prediction: identification and confidence intervals
"What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (for example various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence intervals for the causal relationships in quite general scenarios. We examine the example of structural equation models in more detail and provide sufficient assumptions under which the set of causal predictors becomes identifiable. We further investigate robustness properties of our approach under model misspecification and discuss possible extensions. The empirical properties are studied for various data sets, including large-scale gene perturbation experiments."
to:NB  to_read  causal_inference  causal_discovery  statistics  prediction  regression  buhlmann.peter  meinshausen.nicolai  peters.jonas  heard_the_talk  re:ADAfaEPoV  to_teach:undergrad-ADA 
may 2018 by cshalizi
Family Ruptures, Stress, and the Mental Health of the Next Generation
"This paper studies how in utero exposure to maternal stress from family ruptures affects later mental health. We find that prenatal exposure to the death of a maternal relative increases take-up of ADHD medications during childhood and anti-anxiety and depression medications in adulthood. Further, family ruptures during pregnancy depress birth outcomes and raise the risk of perinatal complications necessitating hospitalization. Our results suggest large welfare gains from preventing fetal stress from family ruptures and possibly from economically induced stressors such as unemployment. They further suggest that greater stress exposure among the poor may partially explain the intergenerational persistence of poverty."

See also an important comment (http://dx.doi.org/10.1257/aer.20161124) and reply (http://dx.doi.org/10.1257/aer.20161605) --- potentially the makings of a very good problem set, if data &c. check out.
to:NB  causal_inference  inequality  economics  to_teach:undergrad-ADA 
march 2018 by cshalizi
How Do Hours Worked Vary with Income? Cross-Country Evidence and Implications
"This paper builds a new internationally comparable database of hours worked to measure how hours vary with income across and within countries. We document that average hours worked per adult are substantially higher in low-income countries than in high-income countries. The pattern of decreasing hours with aggregate income holds for both men and women, for adults of all ages and education levels, and along both the extensive and intensive margin. Within countries, hours worked per worker are also decreasing in the individual wage for most countries, though in the richest countries, hours worked are flat or increasing in the wage. One implication of our findings is that aggregate productivity and welfare differences across countries are larger than currently thought."

--- Last tag depends on availability of replication data.
to:NB  economics  labor  to_teach:undergrad-ADA 
january 2018 by cshalizi
[1711.07137] Nonparametric Double Robustness
"Use of nonparametric techniques (e.g., machine learning, kernel smoothing, stacking) are increasingly appealing because they do not require precise knowledge of the true underlying models that generated the data under study. Indeed, numerous authors have advocated for their use with standard methods (e.g., regression, inverse probability weighting) in epidemiology. However, when used in the context of such singly robust approaches, nonparametric methods can lead to suboptimal statistical properties, including inefficiency and no valid confidence intervals. Using extensive Monte Carlo simulations, we show how doubly robust methods offer improvements over singly robust approaches when implemented via nonparametric methods. We use 10,000 simulated samples and 50, 100, 200, 600, and 1200 observations to investigate the bias and mean squared error of singly robust (g Computation, inverse probability weighting) and doubly robust (augmented inverse probability weighting, targeted maximum likelihood estimation) estimators under four scenarios: correct and incorrect model specification; and parametric and nonparametric estimation. As expected, results show best performance with g computation under correctly specified parametric models. However, even when based on complex transformed covariates, double robust estimation performs better than singly robust estimators when nonparametric methods are used. Our results suggest that nonparametric methods should be used with doubly instead of singly robust estimation techniques."
to:NB  statistics  causal_inference  estimation  nonparametrics  to_teach:undergrad-ADA  kith_and_kin 
january 2018 by cshalizi
Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization
"Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as “Seshat: Global History Databank.” We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history."

--- Contributed, so the last tag applies very forcefully.
to:NB  to_read  comparative_history  complexity_measures  principal_components  to_teach:undergrad-ADA  to_be_shot_after_a_fair_trial 
january 2018 by cshalizi
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models | Neural Computation | MIT Press Journals
"A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties."
to:NB  neural_data_analysis  statistics  to_teach:undergrad-ADA  pillow.jonathan 
december 2017 by cshalizi
Consistency without Inference: Instrumental Variables in Practical Application
"I use the bootstrap to study a comprehensive sample of 1400 instrumental
variables regressions in 32 papers published in the journals of the American
Economic Association. IV estimates are more often found to be falsely significant
and more sensitive to outliers than OLS, while having a higher mean squared error
around the IV population moment. There is little evidence that OLS estimates are
substantively biased, while IV instruments often appear to be irrelevant. In
addition, I find that established weak instrument pre-tests are largely
uninformative and weak instrument robust methods generally perform no better or
substantially worse than 2SLS. "
to:NB  have_read  re:ADAfaEPoV  to_teach:undergrad-ADA  instrumental_variables  causal_inference  regression  statistics  econometrics  via:kjhealy 
november 2017 by cshalizi
[1706.09141] Causal Structure Learning
"Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions. They hence enable predictions under hypothetical interventions, which is important for decision making. The challenging task of learning causal models from data always relies on some underlying assumptions. We discuss several recently proposed structure learning algorithms and their assumptions, and compare their empirical performance under various scenarios."
to:NB  to_read  maathuis.marloes  causal_discovery  statistics  to_teach:undergrad-ADA 
november 2017 by cshalizi
Community and the Crime Decline: The Causal Effect of Local Nonprofits on Violent CrimeAmerican Sociological Review - Patrick Sharkey, Gerard Torrats-Espinosa, Delaram Takyar, 2017
"Largely overlooked in the theoretical and empirical literature on the crime decline is a long tradition of research in criminology and urban sociology that considers how violence is regulated through informal sources of social control arising from residents and organizations internal to communities. In this article, we incorporate the “systemic” model of community life into debates on the U.S. crime drop, and we focus on the role that local nonprofit organizations played in the national decline of violence from the 1990s to the 2010s. Using longitudinal data and a strategy to account for the endogeneity of nonprofit formation, we estimate the causal effect on violent crime of nonprofits focused on reducing violence and building stronger communities. Drawing on a panel of 264 cities spanning more than 20 years, we estimate that every 10 additional organizations focusing on crime and community life in a city with 100,000 residents leads to a 9 percent reduction in the murder rate, a 6 percent reduction in the violent crime rate, and a 4 percent reduction in the property crime rate."

- Last tag conditional on replication data.
to:NB  causal_inference  crime  institutions  via:rvenkat  to_teach:undergrad-ADA 
november 2017 by cshalizi
Empirical prediction intervals improve energy forecasting
"Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks."

--- It's probably presumptuous of me, but I am a bit proud, because the first author learned a lot of these methods from my class...
to:NB  to_read  heard_the_talk  energy  prediction  statistics  to_teach:undergrad-ADA 
august 2017 by cshalizi
FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees
"Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use."

--- I am skeptical about that "simple enough for anyone to understand and use"
to:NB  have_read  decision_trees  heuristics  cognitive_science  R  to_teach:undergrad-ADA  re:ADAfaEPoV 
august 2017 by cshalizi
Evaluations | The Abdul Latif Jameel Poverty Action Lab
"Search our database of 841 randomized evaluations conducted by our affiliates in 80 countries. To browse summaries of key policy recommendations from a subset of these evaluations, visit the Policy Publications tab."
to:NB  causal_inference  experimental_economics  experimental_sociology  statistics  re:ADAfaEPoV  to_teach:undergrad-ADA  economics 
june 2017 by cshalizi
Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China
"The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend—for each season—on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons."

--- Possible teaching example if data is available?
to:NB  ecology  to_teach:undergrad-ADA 
june 2017 by cshalizi
Janzing , Balduzzi , Grosse-Wentrup , Schölkopf : Quantifying causal influences
"Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other n−1 variables. However, quantifying the causal influence of one variable on another one remains a nontrivial question.
"Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy. We then introduce a communication scenario, where edges in a DAG play the role of channels that can be locally corrupted by interventions. Causal strength is then the relative entropy distance between the old and the new distribution.
"Many other measures of causal strength have been proposed, including average causal effect, transfer entropy, directed information, and information flow. We explain how they fail to satisfy the postulates on simple DAGs of ≤3 nodes. Finally, we investigate the behavior of our measure on time-series, supporting our claims with experiments on simulated data."
to:NB  graphical_models  time_series  causality  statistics  information_theory  to_read  re:ADAfaEPoV  to_teach:undergrad-ADA 
december 2016 by cshalizi
[1311.5828] The Splice Bootstrap
"This paper proposes a new bootstrap method to compute predictive intervals for nonlinear autoregressive time series model forecast. This method we call the splice boobstrap as it involves splicing the last p values of a given series to a suitably simulated series. This ensures that each simulated series will have the same set of p time series values in common, a necessary requirement for computing conditional predictive intervals. Using simulation studies we show the methods gives 90% intervals intervals that are similar to those expected from theory for simple linear and SETAR model driven by normal and non-normal noise. Furthermore, we apply the method to some economic data and demonstrate the intervals compare favourably with cross-validation based intervals."
to:NB  bootstrap  time_series  statistics  prediction  to_teach:undergrad-ADA  re:ADAfaEPoV  to_read 
december 2016 by cshalizi
Illness as indicator | The Economist
"Polling data suggests that on the whole, Mr Trump’s supporters are not particularly down on their luck: within any given level of educational attainment, higher-income respondents are more likely to vote Republican. But what the geographic numbers do show is that the specific subset of Mr Trump’s voters that won him the election—those in counties where he outperformed Mr Romney by large margins—live in communities that are literally dying."

--- Replication files available?
track_down_references  us_politics  trump.donald  whats_gone_wrong_with_america  to_teach:undergrad-ADA 
november 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
Surfeit and surface | Big Data & Society
This is awesome. (But it's also completely compatible with causal inference!) Also, the cultural references will probably require footnotes in just 10 years.
social_science_methodology  sociology  data_mining  levi.john_martin  have_read  via:phnk  to_teach:undergrad-ADA  to_teach:data-mining  re:any_p-value_distinguishable_from_zero_is_insufficiently_informative  to:blog 
april 2016 by cshalizi
Hardle , Marron : Bootstrap Simultaneous Error Bars for Nonparametric Regression
"Simultaneous error bars are constructed for nonparametric kernel estimates of regression functions. The method is based on the bootstrap, where resampling is done from a suitably estimated residual distribution. The error bars are seen to give asymptotically correct coverage probabilities uniformly over any number of gridpoints. Applications to an economic problem are given and comparison to both pointwise and Bonferroni-type bars is presented through a simulation study."
to:NB  to_read  bootstrap  confidence_sets  regression  nonparametrics  statistics  to_teach:undergrad-ADA  re:ADAfaEPoV 
april 2016 by cshalizi
Statistically controlling for confounding constructs is harder than you think
"Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement- level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity."
to:NB  have_read  measurement  social_measurement  social_science_methodology  psychometrics  econometrics  graphical_models  statistics  to_teach:undergrad-ADA  re:ADAfaEPoV  yarkoni.tal  to:blog 
march 2016 by cshalizi
School Finance Reform and the Distribution of Student Achievement
"We study the impact of post-1990 school finance reforms, during the so-called "adequacy" era, on gaps in spending and achievement between high-income and low-income school districts. Using an event study design, we find that reform events--court orders and legislative reforms--lead to sharp, immediate, and sustained increases in absolute and relative spending in low-income school districts. Using representative samples from the National Assessment of Educational Progress, we also find that reforms cause gradual increases in the relative achievement of students in low-income school districts, consistent with the goal of improving educational opportunity for these students. The implied effect of school resources on educational achievement is large."

- Last tag depends on replication data, which might not be available.
to:NB  education  inequality  us_politics  causal_inference  to_teach:undergrad-ADA  via:jbdelong 
february 2016 by cshalizi
Information, Inequality, and Mass Polarization: Ideology in Advanced Democracies
"Growing polarization in the American Congress is closely related to rising income inequality. Yet there has been no corresponding polarization of the U.S. electorate, and across advanced democracies, mass polarization is negatively related to income inequality. To explain this puzzle, we propose a comparative political economy model of mass polarization in which the same institutional factors that generate income inequality also undermine political information. We explain why more voters then place themselves in the ideological center, hence generating a negative correlation between mass polarization and inequality. We confirm these conjectures on individual-level data for 20 democracies, and we then show that democracies cluster into two types: one with high inequality, low mass polarization, and polarized and right-shifted elites (e.g., the United States); and the other with low inequality and high mass polarization with left-shifted elites (e.g., Sweden). This division reflects long-standing differences in educational systems, the role of unions, and social networks."

--- Replication data available?
political_economy  political_science  social_networks  unions  political_parties  inequality  class_struggles_in_america  whats_gone_wrong_with_america  re:democratic_cognition  democracy  via:henry_farrell  to_read  to_teach:undergrad-ADA 
february 2016 by cshalizi
Homicide in Eighteenth-Century Scotland: Numbers and Theories - Edinburgh University Press
"The purpose of this article is to address the lacuna in our knowledge of the extent of interpersonal violence in eighteenth-century Scotland, with particular reference to homicide, and in doing so use these findings to examine the theoretical and empirical issues that have dominated historical discourse regarding this phenomenon over the last few decades. Essentially, it seeks to challenge widely held explanations for the alleged long-term decline in homicide, arguing that incidences of murder in the eighteenth century were affected more by political tensions and socio-economic dislocation than by cultural changes in taste and manners. It also criticises the methodological weaknesses evident in longitudinal studies of homicide and tries to resolve them in two ways: firstly, by adjusting the homicide rate to take account of the rises and falls in population in the period 1700–1799; and, secondly, by providing national data rather than relying on extrapolating national trends from local or regional studies. Finally, it is argued that the main assumptions of historians working in the field of homicide studies are in the light of evidence for Scotland in need of revision as data from there provide little support for a linear fall in the level of homicides, or a link with shifts in sentiment and/or taste as put forward by those influenced by the civilising theories of Norbert Elias."

--- Smoothing over time, with a generalized additive model (though only one predictor variable, so really a spline + a fancy link function). Perhaps usable as an example.
to:NB  to_read  violence  statistics  early_modern_european_history  the_civilizing_process  scotland  to_teach:undergrad-ADA 
february 2016 by cshalizi
Large Sample Properties of Matching Estimators for Average Treatment Effects - Abadie - 2005 - Econometrica - Wiley Online Library
"Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N1/2-consistent in general and describe conditions under which matching estimators do attain N1/2-consistency. Second, we show that even in settings where matching estimators are N1/2-consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R."

--- An unkind version of this would be "matching is what happens when you do nearest-neighbor regression, and you forget that the bias-variance tradeoff is a _tradeoff_."

(Ungated version: http://www.ksg.harvard.edu/fs/aabadie/smep.pdf)

(ADA note: reference in the causal-estimation chapter, re connection between matching and nearest neighbors)
to:NB  statistics  estimation  causal_inference  regression  to_teach:undergrad-ADA  have_read  matching 
february 2016 by cshalizi
AEAweb: AER (95,3) p. 546 - The Rise of Europe: Atlantic Trade, Institutional Change, and Economic Growth
"The rise of Western Europe after 1500 is due largely to growth in countries with access to the Atlantic Ocean and with substantial trade with the New World, Africa, and Asia via the Atlantic. This trade and the associated colonialism affected Europe not only directly, but also indirectly by inducing institutional change. Where "initial" political institutions (those established before 1500) placed significant checks on the monarchy, the growth of Atlantic trade strengthened merchant groups by constraining the power of the monarchy, and helped merchants obtain changes in institutions to protect property rights. These changes were central to subsequent economic growth."
to:NB  economics  economic_history  institutions  economic_growth  to_teach:undergrad-ADA  via:jbdelong  have_read 
january 2016 by cshalizi
Does data splitting improve prediction? - Springer
"Data splitting divides data into two parts. One part is reserved for model selection. In some applications, the second part is used for model validation but we use this part for estimating the parameters of the chosen model. We focus on the problem of constructing reliable predictive distributions for future observed values. We judge the predictive performance using log scoring. We compare the full data strategy with the data splitting strategy for prediction. We show how the full data score can be decomposed into model selection, parameter estimation and data reuse costs. Data splitting is preferred when data reuse costs are high. We investigate the relative performance of the strategies in four simulation scenarios. We introduce a hybrid estimator that uses one part for model selection but both parts for estimation. We argue that a split data analysis is prefered to a full data analysis for prediction with some exceptions."

--- Ungated: http://arxiv.org/abs/1301.2983
statistics  regression  prediction  model_selection  faraway.j.j.  re:ADAfaEPoV  to_teach:undergrad-ADA  have_read  to_teach:linear_models  in_NB 
january 2016 by cshalizi
CRAN - Package ridge
"Linear and logistic ridge regression for small data sets and genome-wide SNP data"
R  regression  statistics  ridge_regression  to_teach:undergrad-ADA  to_teach:linear_models 
october 2015 by cshalizi
Untangling the sources of racial wealth inequality in the United States - Equitable Growth Equitable Growth
Suppose --- work with me here --- that one of the things which makes it easier to buy a home is _having parents with wealth_, who can _pass along some of that wealth while they are alive_. (Hey, it's a hypothesis.) What, exactly, do you learn from the coefficient on "inheritance" in a regression which controls for "homeownership"? Similarly, suppose one of the things wealthier parents buy for their children is _access to education_, leading to job opportunities, and _direct access to job opportunities_. Again, what do you learn in a regression which "controls for" (I can't help the scare quotes) income?
economics  inequality  have_read  track_down_references  racism  the_american_dilemma  transmission_of_inequality  to_teach:undergrad-ADA  to_teach:linear_models 
october 2015 by cshalizi
globalinequality: Did socialism keep capitalism equal?
Some econometric evidence for one of my pet-crank notions. The regression specifications look dubious, however.
cold_war  political_economy  socialism  economics  inequality  to:blog  track_down_references  to_teach:linear_models  to_teach:undergrad-ADA 
august 2015 by cshalizi
Science Isn’t Broken | FiveThirtyEight
I like the idea of having researchers compete to throw all sorts of different modeling choices at the same data, and the initial example is cool.
science  science_as_a_social_process  have_read  to_teach:undergrad-ADA 
august 2015 by cshalizi
Heat Wave: A Social Autopsy of Disaster in Chicago, Klinenberg
"Heat waves in the United States kill more people during a typical year than all other natural disasters combined. Until now, no one could explain either the overwhelming number or the heartbreaking manner of the deaths resulting from the 1995 Chicago heat wave. Meteorologists and medical scientists have been unable to account for the scale of the trauma, and political officials have puzzled over the sources of the city's vulnerability. In Heat Wave, Eric Klinenberg takes us inside the anatomy of the metropolis to conduct what he calls a "social autopsy," examining the social, political, and institutional organs of the city that made this urban disaster so much worse than it ought to have been."
to:NB  books:noted  chicago  disasters  sociology  to_teach:undergrad-ADA  books:owned 
june 2015 by cshalizi
AEAweb: AER (105,6) p. 1738 - Trafficking Networks and the Mexican Drug War
"Drug trade-related violence has escalated dramatically in Mexico since 2007, and recent years have also witnessed large-scale efforts to combat trafficking, spearheaded by Mexico's conservative PAN party. This study examines the direct and spillover effects of Mexican policy toward the drug trade. Regression discontinuity estimates show that drug-related violence increases substantially after close elections of PAN mayors. Empirical evidence suggests that the violence reflects rival traffickers' attempts to usurp territories after crackdowns have weakened incumbent criminals. Moreover, the study uses a network model of trafficking routes to show that PAN victories divert drug traffic, increasing violence along alternative drug routes."

--- Look at data set & c. to see if this could become a problem set.
to:NB  drugs  crime  causal_inference  mexico  economics  to_teach:undergrad-ADA 
june 2015 by cshalizi
Welcome to NB
Last tag is tentative but this seems like a very interesting tool.
teaching  social_media  to_teach:undergrad-ADA 
may 2015 by cshalizi
[1505.02452] Design and interpretation of studies: relevant concepts from the past and some extensions
"Principles for the planning and analysis of observational studies, as suggested by W.G Cochran in 1972, are discussed and compared to additional methodological developments since then."
to:NB  statistics  cox.d.r  wemuth.nanny  to_teach:undergrad-ADA  to_read 
may 2015 by cshalizi
CRAN - Package AlgDesign
"Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact."
R  experimental_design  statistics  to_teach:undergrad-ADA 
april 2015 by cshalizi
Welcome to the CRCNS data sharing website — CRCNS.org
Sharing neural data; some of the data sets require an (anonymous) login.

--- See about using one of the movement data sets for a multivariate-analysis problem set (or exam?).
neuroscience  data_sets  to_teach:undergrad-ADA 
march 2015 by cshalizi
Dead and Alive: Beliefs in Contradictory Conspiracy Theories
"Conspiracy theories can form a monological belief system: A self-sustaining worldview comprised of a network of mutually supportive beliefs. The present research shows that even mutually incompatible conspiracy theories are positively correlated in endorsement. In Study 1 (n = 137), the more participants believed that Princess Diana faked her own death, the more they believed that she was murdered. In Study 2 (n = 102), the more participants believed that Osama Bin Laden was already dead when U.S. special forces raided his compound in Pakistan, the more they believed he is still alive. Hierarchical regression models showed that mutually incompatible conspiracy theories are positively associated because both are associated with the view that the authorities are engaged in a cover-up (Study 2). The monological nature of conspiracy belief appears to be driven not by conspiracy theories directly supporting one another but by broader beliefs supporting conspiracy theories in general."

--- I'd want to look very carefully at the numerical data to make sure this isn't being driven by a few people who are crazy (even once you allow for their being into conspiracy theories). In fact, this sounds like a situation where you'd really want to look carefully at protocols collected from the interviewees... Last tag conditional on the authors responding positively to my query about access to the data.
to:NB  have_skimmed  surveys  hierarchical_statistical_models  conspiracy_theories  sociology  to_teach:undergrad-ADA  psychology  natural_history_of_truthiness 
february 2015 by cshalizi
Overdispersion Diagnostics for Generalized Linear Models on JSTOR
"Generalized linear models (GLM's) are simple, convenient models for count data, but they assume that the variance is a specified function of the mean. Although overdispersed GLM's allow more flexible mean-variance relationships, they are often not as simple to interpret nor as easy to fit as standard GLM's. This article introduces a convexity plot, or C plot for short, that detects overdispersion and relative variance curves and relative variance tests that help to understand the nature of the overdispersion. Convexity plots sometimes detect overdispersion better than score tests, and relative variance curves and tests sometimes distinguish the source of the overdispersion better than score tests."
in_NB  statistics  regression  model_checking  kith_and_kin  roeder.kathryn  to_teach:undergrad-ADA  have_read 
february 2015 by cshalizi
What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?
"We study the evolution of individual labor earnings over the life cycle, using a large panel data set of earnings histories drawn from U.S. administrative records. Using fully nonparametric methods, our analysis reaches two broad conclusions. First, earnings shocks display substantial deviations from lognormality—the standard assumption in the literature on incomplete markets. In particular, earnings shocks display strong negative skewness and extremely high kurtosis—as high as 30 compared with 3 for a Gaussian distribution. The high kurtosis implies that, in a given year, most individuals experience very small earnings shocks, and a small but non-negligible number experience very large shocks. Second, these statistical properties vary significantly both over the life cycle and with the earnings level of individuals. We also estimate impulse response functions of earnings shocks and find important asymmetries: Positive shocks to high-income individuals are quite transitory, whereas negative shocks are very persistent; the opposite is true for low-income individuals. Finally, we use these rich sets of moments to estimate econometric processes with increasing generality to capture these salient features of earnings dynamics."

--- Last tag conditional on what exactly is in the "data appendix" at https://fguvenendotcom.files.wordpress.com/2014/04/moments_for_publication.xls
to:NB  to_read  economics  inequality  heavy_tails  to_teach:undergrad-ADA  statistics  great_risk_shift 
february 2015 by cshalizi
On the Interpretation of Instrumental Variables in the Presence of Specification Errors
"The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist."

--- I think they are too quick to dismiss non-parametric IV; if what one wants is consistent estimates of the partial derivatives at a given point, you _can_ get that by (e.g.) splines or locally linear regression. Need to think through this in terms of Pearl's graphical definition of IVs.
in_NB  instrumental_variables  misspecification  regression  linear_regression  causal_inference  statistics  econometrics  via:jbdelong  have_read  to_teach:undergrad-ADA  re:ADAfaEPoV 
february 2015 by cshalizi
[1312.7851] Effective Degrees of Freedom: A Flawed Metaphor
"To most applied statisticians, a fitting procedure's degrees of freedom is synonymous with its model complexity, or its capacity for overfitting to data. In particular, it is often used to parameterize the bias-variance tradeoff in model selection. We argue that, contrary to folk intuition, model complexity and degrees of freedom are not synonymous and may correspond very poorly. We exhibit and theoretically explore various examples of fitting procedures for which degrees of freedom is not monotonic in the model complexity parameter, and can exceed the total dimension of the response space. Even in very simple settings, the degrees of freedom can exceed the dimension of the ambient space by an arbitrarily large amount. We show the degrees of freedom for any non-convex projection method can be unbounded."

--- I have never really liked "degrees of freedom"...

--- ETA after reading: to be clear, no one is arguing about "effective degrees of freedom", in the sense of Efron (1986), telling us about over-fitting. The demonstrations here are that the geometric metaphor behind "degrees of freedom", while holding for linear models (without model selection), becomes very misleading in other contexts. Now, since I prefer to think of model selection in terms of capacity to over-fit, rather than the number of adjustable knobs...
to:NB  model_selection  regression  statistics  hastie.trevor  to_teach:undergrad-ADA  have_read  convexity 
january 2015 by cshalizi
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