Graphical models for causation, and the identification problem

Evaluation Review
David A Freedman

Abstract

This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.

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Citations

Jun 8, 2007·Bulletin of Mathematical Biology·E ShochatL A Segel
Oct 2, 2012·Journal of the Air & Waste Management Association·Goran Krstić
Mar 12, 2013·Journal of the Air & Waste Management Association·Goran Krstić
Mar 2, 2006·Risk Analysis : an Official Publication of the Society for Risk Analysis·Louis Anthony Tony Cox
Nov 30, 2012·Journal of the Royal Statistical Society. Series A, (Statistics in Society)·Odd O AalenBruno Ledergerber
May 31, 2013·Risk Analysis : an Official Publication of the Society for Risk Analysis·Louis Anthony Tony Cox
Jun 26, 2013·Risk Analysis : an Official Publication of the Society for Risk Analysis·Louis Anthony Tony Cox
Apr 12, 2013·Critical Reviews in Toxicology·Louis Anthony Tony CoxD Wayne Berman
Jan 13, 2015·Annals of Epidemiology·Louis Anthony Tony Cox, Douglas A Popken
Aug 1, 2014·Environmental Health Perspectives·Carol J BurnsKun Don Yi

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