Causal Inference from Graphical Models

This article surveys modern developments within graphical models concerned with using these as a basis for discussing and inferring about causal relationships. It is in particular concerned with the calculus of intervention eeects and their identiiability from observational or experimental studies. The article will appear as a chapter in: O.

[1]  Illtyd Trethowan.  Causality , 1938 .

[2]  T. Haavelmo.  The Statistical Implications of a System of Simultaneous Equations , 1943 .

[3]  Robert H. Strotz,et al.  Recursive versus non-recursive systems: An attempt at a synthesis , 2017 .

[4]  G. Box.  Use and Abuse of Regression , 1966 .

[5]  J. M. Hammersley,et al.  Markov fields on finite graphs and lattices , 1971 .

[6]  S. Goldfeld,et al.  Non Linear Methods in Econometrics , 1974 .

[7]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[8]  A. Goldberger,et al.  Structural Equation Models in the Social Sciences. , 1974 .

[9]  Donald Nute,et al.  Counterfactuals , 1975, Notre Dame J. Formal Log..

[10]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[11]  D. A. Kenny,et al.  Correlation and Causation , 1937, Wilmott.

[12]  D. A. Kenny,et al.  Correlation and Causation. , 1982 .

[13]  T. Speed,et al.  Structural Analysis of Multivariate Data: A Review , 1982 .

[14]  T. Speed,et al.  Recursive causal models , 1984, Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics.

[15]  Judea Pearl,et al.  A Constraint-Propagation Approach to Probabilistic Reasoning , 1985, UAI.

[16]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[17]  P. Holland.  Statistics and Causal Inference , 1985 .

[18]  Ross D. Shachter.  Evaluating Influence Diagrams , 1986, Oper. Res..

[19]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[20]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[21]  Prakash P. Shenoy,et al.  Axioms for probability and belief-function proagation , 1990, UAI.

[22]  Dan Geiger,et al.  On the logic of causal models , 2013, UAI.

[23]  Judea Pearl,et al.  Causal networks: semantics and expressiveness , 2013, UAI.

[24]  Kevin T. Kelly,et al.  Discovering Causal Structure. , 1989 .

[25]  Jim Q. Smith.  Influence diagrams for Bayesian decision analysis , 1989 .

[26]  Douglas W. Nychka,et al.  Discovering Causal Structure , 1989 .

[27]  C. Manski.  Nonparametric Bounds on Treatment Effects , 1989 .

[28]  A. Gallant.  Non-linear Methods in Econometrics , 1990 .

[29]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[30]  M. Frydenberg.  Marginalization and Collapsibility in Graphical Interaction Models , 1990 .

[31]  M. Frydenberg.  The chain graph Markov property , 1990 .

[32]  Steffen L. Lauritzen,et al.  Independence properties of directed markov fields , 1990, Networks.

[33]  B. Efron,et al.  Compliance as an Explanatory Variable in Clinical Trials , 1991 .

[34]  S. Zeger,et al.  On estimating efficacy from clinical trials. , 1991, Statistics in medicine.

[35]  R. M. Oliver,et al.  Influence diagrams, belief nets and decision analysis , 1992 .

[36]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[37]  David J. Spiegelhalter,et al.  Bayesian analysis in expert systems , 1993 .

[38]  Ross D. Shachter,et al.  Decision-Theoretic Foundations for Causal Reasoning , 1995, J. Artif. Intell. Res..

[39]  Christopher Meek,et al.  Strong completeness and faithfulness in Bayesian networks , 1995, UAI.

[40]  Peter Spirtes,et al.  Directed Cyclic Graphical Representations of Feedback Models , 1995, UAI.

[41]  Judea Pearl,et al.  Causal inference from indirect experiments , 1995, Artif. Intell. Medicine.

[42]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[43]  Thomas S. Richardson,et al.  Automated discovery of linear feedback models , 1996 .

[44]  J. Koster,et al.  Markov properties of nonrecursive causal models , 1996 .

[45]  Glenn Shafer,et al.  The art of causal conjecture , 1996 .

[46]  James J. Heckman,et al.  Identification of Causal Effects Using Instrumental Variables: Comment , 1996 .

[47]  David Maxwell Chickering,et al.  A Clinician's Tool for Analyzing Non-Compliance , 1996, AAAI/IAAI, Vol. 2.

[48]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[49]  D. Freedman.  From Association to Causation via Regression , 1997 .

[50]  J. Pearl,et al.  Bounds on Treatment Effects from Studies with Imperfect Compliance , 1997 .

[51]  D. Rubin,et al.  Bayesian inference for causal effects in randomized experiments with noncompliance , 1997 .

[52]  J. Pearl.  Graphs, Causality, and Structural Equation Models , 1998 .

[53]  P. Spirtes,et al.  Using Path Diagrams as a Structural Equation Modeling Tool , 1998 .

[54]  Michael I. Jordan.  Graphical Models , 2003 .

[55]  J. Koster.  On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors , 1999 .

[56]  Gregory F. Cooper,et al.  An overview of the representation and discovery of causal relationships using Bayesian networks , 1999 .

[57]  J. Robins,et al.  On the impossibility of inferring causation from association without background knowledge , 1999 .

[58]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[59]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[60]  A. Dawid.  Causal Inference without Counterfactuals , 2000 .

[61]  Mtw,et al.  Computation, causation, and discovery , 2000 .

[62]  Steffen L. Lauritzen,et al.  Representing and Solving Decision Problems with Limited Information , 2001, Manag. Sci..