Causal Inference

Judea Pearl
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:39-58, 2010.

Abstract

This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-pearl10a, title = {Causal Inference}, author = {Pearl, Judea}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {39--58}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/pearl10a/pearl10a.pdf}, url = {https://proceedings.mlr.press/v6/pearl10a.html}, abstract = {This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.} }
Endnote
%0 Conference Paper %T Causal Inference %A Judea Pearl %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-pearl10a %I PMLR %P 39--58 %U https://proceedings.mlr.press/v6/pearl10a.html %V 6 %X This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.
RIS
TY - CPAPER TI - Causal Inference AU - Judea Pearl BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-pearl10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 39 EP - 58 L1 - http://proceedings.mlr.press/v6/pearl10a/pearl10a.pdf UR - https://proceedings.mlr.press/v6/pearl10a.html AB - This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another. ER -
APA
Pearl, J.. (2010). Causal Inference. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:39-58 Available from https://proceedings.mlr.press/v6/pearl10a.html.

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