causality   1324

« earlier    

(7) (PDF) A Methodology for Constructing Collective Causal Maps*
Causal maps are an essential tool for managers who seek to improve complex systems in the areas of quality, strategy, and information systems. These causal maps are known by many names, including Ishikawa (fishbone) diagrams, cause-and-effect diagrams, impact wheels, issue trees, strategy maps, and risk-assessment mapping tools.
causality  mapping 
yesterday by zryb
[1904.02995] When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents
"An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in."
to:NB  causality  agent-based_models  philosophy_of_mind  mijnheer_spinoza_mijnheer_benedictus_spinoza_to_the_courtesy_phone_please 
2 days ago by cshalizi
Inferring causation from time series in Earth system sciences | Nature Communications
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond.
causality 
4 days ago by zryb
The Seven Tools of Causal Inference, with Reflections on Machine Learning | March 2019 | Communications of the ACM
By Judea Pearl
Communications of the ACM, March 2019,

I call the mathematical framework that led to this transformation "structural causal models" (SCM), which consists of three parts: graphical models, structural equations, and counterfactual and interventional logic. Graphical models serve as a language for representing what agents know about the world. Counterfactuals help them articulate what they wish to know. And structural equations serve to tie the two together in a solid semantics.
abduction  causality 
6 days ago by zryb
The Benefits of Reading to Children, Tested With a Data Pool of One - Freakonomics Freakonomics
"One of the most controversial small points in Freakonomics was the claim that early childhood test scores are not correlated to the amount a child is read to at home."
parenting  correlation  causality  psychology 
19 days ago by pmigdal
History is Sociology: All Arguments are Counterfactuals - Gould - 2019 - Journal of Historical Sociology - Wiley Online Library
contend that all arguments are counterfactual (or, what is the same thing, comparative). We cannot chose to do counterfactual history; we chose either to make coherent arguments, which are counterfactual, or to make assertions that are logically flawed, are no arguments at all. As Durkheim put it, “Comparative sociology is not a special branch of sociology; it is sociology itself, in so far as it ceases to be purely descriptive and aspires to account for facts.”1 The methodology we adopt is secondary; it should be selected to best address the problems we examine.2
history  sociology  causality  counterfactuals 
21 days ago by demetriodor
The Real Gold Standard: Measuring Counterfactual Worlds That Matter Most to Social Science and Policy | Annual Review of Criminology
"The randomized experiment has achieved the status of the gold standard for estimating causal effects in criminology and the other social sciences. Although causal identification is indeed important and observational data present numerous challenges to causal inference, we argue that conflating causality with the method used to identify it leads to a cognitive narrowing that diverts attention from what ultimately matters most—the difference between counterfactual worlds that emerge as a consequence of their being subjected to different treatment regimes applied to all eligible population members over a sustained period of time. To address this system-level and long-term challenge, we develop an analytic framework for integrating causality and policy inference that accepts the mandate of causal rigor but is conceptually rather than methodologically driven. We then apply our framework to two substantive areas that have generated high-visibility experimental research and that have considerable policy influence: (a) hot-spots policing and (b) the use of housing vouchers to reduce concentrated disadvantage and thereby crime. After reviewing the research in these two areas in light of our framework, we propose a research path forward and conclude with implications for the interplay of theory, data, and causal understanding in criminology and other social sciences."
to:NB  causal_inference  causality  social_science_methodology  statistics  nagin.dan  kith_and_kin  re:ADAfaEPoV 
24 days ago by cshalizi
On the Interpretation of do(x) : Journal of Causal Inference
"This paper provides empirical interpretation of the do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally eligible to receive the do(x) operator and to produce useful information for decision makers."
causality  via:cshalizi 
26 days ago by arsyed
On the Interpretation of do(x) : Journal of Causal Inference
"This paper provides empirical interpretation of the do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally eligible to receive the do(x) operator and to produce useful information for decision makers."
to:NB  causality  pearl.judea  re:ADAfaEPoV  to_read 
26 days ago by cshalizi
Time and causality across the sciences
"This book, geared toward academic researchers and graduate students, brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Research across disciplines shows that the relationship is much more complex than that. This book explores what that means for both the metaphysics and epistemology of causes - what they are and how we can find them. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models."
books  causality  causal-systems  time  samantha-kleinberg  via:cshalizi 
4 weeks ago by arsyed
Time and causality across the sciences
"This book, geared toward academic researchers and graduate students, brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Research across disciplines shows that the relationship is much more complex than that. This book explores what that means for both the metaphysics and epistemology of causes - what they are and how we can find them. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models."
to:NB  books:noted  causal_inference  causality  arrow_of_time  kleinberg.samantha 
5 weeks ago by cshalizi
A Decision Theoretic Approach to A/B Testing
- obtain better NHST thresholds for sequential tests via Bayesian stats
causality  experiments  data-science 
6 weeks ago by patrick-dd
Semiparametric theory
In this paper we give a brief review of semiparametric theory, using as a running
example the common problem of estimating an average causal effect. Semiparametric models allow at least part of the data-generating process to be unspecified and
unrestricted, and can often yield robust estimators that nonetheless behave similarly
to those based on parametric likelihood assumptions, e.g., fast rates of convergence
to normal limiting distributions. We discuss the basics of semiparametric theory,
focusing on influence functions.
data-science  econometrics  statistics  causality 
6 weeks ago by patrick-dd

« earlier    

related tags

abduction  acm  agent-based_models  agents  ai  analysis  arrow_of_time  article  artificial-intelligence  arxiv  astrology  async  bandit  bandits  book  books  books:noted  by:judepearl  by:leonbottou  causal-inference  causal-learning  causal-systems  causal_inference  causalinference  classes  classification  clock  common  complexity  computer_architecture  computer_engineering  confounding  consistency  correlation  counterfactual-learning  counterfactuals  course  culture  d-in-d  dag  dashdone  data-science  data  datascience  deep-learning  deeplearning  deepmind  diagram  difussion  discrimination  distributed-systems  distributed.systems  distributed_systems  econometrics  einstein  epistemology  evaluation-measures  experiments  explanation  fairness  front-door  gender  gr  gwas  hard  health  history  humor  inference  intervention  jean  kith_and_kin  kleinberg.samantha  lamport  law  learning  machine-learning  machine_learning  machinelearning  manipulation  mapping  mathematics  mechanism  media  mediation  medical  mental  meta-learning  mijnheer_spinoza_mijnheer_benedictus_spinoza_to_the_courtesy_phone_please  modeling  nagin.dan  non-manipulability  parenting  path_specific_effects  pearl.judea  pearl  people  philosophy  philosophy_of_mind  philosophy_of_science  probabilistic-programming  probabilistic  probability  problems  psychology  python  pytorch  race  re:adafaepov  recommender  regression_discontinuity  reinforcement-learning  relative  relativity  reproducibleresearch  research-article  research  researchers  review  s-parameter  safety  samantha-kleinberg  science  sense  sequential  side-effects  singularity  social  social_science_methodology  socialmedia  socialsciences  sociology  statistics  stats  stories  study-group  system_architecture  systems_engineering  teens  time  tipsntricks  to-understand  to:nb  to:pgc  to_read  tutorial  twenge  victor-veitch 

Copy this bookmark:



description:


tags: