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On identifying causal effects

On identifying causal effects
On identifying causal effects
A variety of questions in causal inference can be represented as probability distributions over hypothetical worlds where idealized randomized experiments known as interventions have taken place. Some such questions are really questions of causal effect of a particular intervention, while others are counterfactual and consider results of interventions which violate the state of affairs actually observed. Randomized experiments are expensive and often illegal. It is therefore imperative to find ways of evaluating, or identifying causal effect and counterfactual questions from available information, and causal assumptions. In this paper, we review the state of the art in identification of causal effects and related counterfactual quantities in the framework of graphical causal models, a formalism where a causal domain of interest is represented by directed acyclic graphs with vertices representing variables of interest, and arrows representing direct causal influences.
978-1904987659
College Publications
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Tian, Jin
39c8ca1c-a21d-4d24-9d89-78479c750ddf
Dechter, Rina
Geffner, Hector
Halpern, Joseph Y.
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Tian, Jin
39c8ca1c-a21d-4d24-9d89-78479c750ddf
Dechter, Rina
Geffner, Hector
Halpern, Joseph Y.

Shpitser, Ilya and Tian, Jin (2010) On identifying causal effects. In, Dechter, Rina, Geffner, Hector and Halpern, Joseph Y. (eds.) Heuristics, Probability, and Causality: A Tribute to Judea Pearl. London, GB. College Publications.

Record type: Book Section

Abstract

A variety of questions in causal inference can be represented as probability distributions over hypothetical worlds where idealized randomized experiments known as interventions have taken place. Some such questions are really questions of causal effect of a particular intervention, while others are counterfactual and consider results of interventions which violate the state of affairs actually observed. Randomized experiments are expensive and often illegal. It is therefore imperative to find ways of evaluating, or identifying causal effect and counterfactual questions from available information, and causal assumptions. In this paper, we review the state of the art in identification of causal effects and related counterfactual quantities in the framework of graphical causal models, a formalism where a causal domain of interest is represented by directed acyclic graphs with vertices representing variables of interest, and arrows representing direct causal influences.

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Published date: 5 February 2010
Organisations: Statistics

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Local EPrints ID: 350591
URI: http://eprints.soton.ac.uk/id/eprint/350591
ISBN: 978-1904987659
PURE UUID: 084d6d7e-1793-497c-b402-b42bca21490e

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Date deposited: 08 Apr 2013 12:51
Last modified: 14 Mar 2024 13:29

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Contributors

Author: Ilya Shpitser
Author: Jin Tian
Editor: Rina Dechter
Editor: Hector Geffner
Editor: Joseph Y. Halpern

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