Causal Discovery from Data in the Presence of Selection Bias

Gregory F. Cooper
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:140-150, 1995.

Abstract

Recent research advances have made it possible to consider using observational data to infer causal relationships among measured variables. Selection bias results from the observation of entities that are not representative of the entities that are generated by a causal process of interest. This paper shows that we can sometimes detect the presence of selection bias in observational data. The paper also demonstrates how selection bias can hinder the discovery of causal relationships from observational data. As we will describe, the use of experimental data (e.g., data from randomized, controlled trials) to discover causal relationships can be susceptible as well to problems involving selection bias. We offer suggestions for how to proceed with causal discovery in the face of selection bias.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-cooper95a, title = {Causal Discovery from Data in the Presence of Selection Bias}, author = {Cooper, Gregory F.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {140--150}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/cooper95a/cooper95a.pdf}, url = {https://proceedings.mlr.press/r0/cooper95a.html}, abstract = {Recent research advances have made it possible to consider using observational data to infer causal relationships among measured variables. Selection bias results from the observation of entities that are not representative of the entities that are generated by a causal process of interest. This paper shows that we can sometimes detect the presence of selection bias in observational data. The paper also demonstrates how selection bias can hinder the discovery of causal relationships from observational data. As we will describe, the use of experimental data (e.g., data from randomized, controlled trials) to discover causal relationships can be susceptible as well to problems involving selection bias. We offer suggestions for how to proceed with causal discovery in the face of selection bias.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Causal Discovery from Data in the Presence of Selection Bias %A Gregory F. Cooper %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-cooper95a %I PMLR %P 140--150 %U https://proceedings.mlr.press/r0/cooper95a.html %V R0 %X Recent research advances have made it possible to consider using observational data to infer causal relationships among measured variables. Selection bias results from the observation of entities that are not representative of the entities that are generated by a causal process of interest. This paper shows that we can sometimes detect the presence of selection bias in observational data. The paper also demonstrates how selection bias can hinder the discovery of causal relationships from observational data. As we will describe, the use of experimental data (e.g., data from randomized, controlled trials) to discover causal relationships can be susceptible as well to problems involving selection bias. We offer suggestions for how to proceed with causal discovery in the face of selection bias. %Z Reissued by PMLR on 01 May 2022.
APA
Cooper, G.F.. (1995). Causal Discovery from Data in the Presence of Selection Bias. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:140-150 Available from https://proceedings.mlr.press/r0/cooper95a.html. Reissued by PMLR on 01 May 2022.

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