On a Class of Bias-Amplifying Variables that Endanger Effect Estimates

Judea Pearl
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:424-431, 2010.

Abstract

This note deals with a class of variables that, if conditioned on, tends to amplify confound- ing bias in the analysis of causal effects. This class, independently discovered by Bhat- tacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treat- ment selection than on the outcome. We offer a simple derivation and an intuitive explana- tion of this phenomenon and then extend the analysis to non linear models. We show that:

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-pearl10b, title = {On a Class of Bias-Amplifying Variables that Endanger Effect Estimates}, author = {Pearl, Judea}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {424--431}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/pearl10b/pearl10b.pdf}, url = {https://proceedings.mlr.press/r8/pearl10b.html}, abstract = {This note deals with a class of variables that, if conditioned on, tends to amplify confound- ing bias in the analysis of causal effects. This class, independently discovered by Bhat- tacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treat- ment selection than on the outcome. We offer a simple derivation and an intuitive explana- tion of this phenomenon and then extend the analysis to non linear models. We show that:}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T On a Class of Bias-Amplifying Variables that Endanger Effect Estimates %A Judea Pearl %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-pearl10b %I PMLR %P 424--431 %U https://proceedings.mlr.press/r8/pearl10b.html %V R8 %X This note deals with a class of variables that, if conditioned on, tends to amplify confound- ing bias in the analysis of causal effects. This class, independently discovered by Bhat- tacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treat- ment selection than on the outcome. We offer a simple derivation and an intuitive explana- tion of this phenomenon and then extend the analysis to non linear models. We show that: %Z Reissued by PMLR on 04 October 2026.
APA
Pearl, J.. (2010). On a Class of Bias-Amplifying Variables that Endanger Effect Estimates. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:424-431 Available from https://proceedings.mlr.press/r8/pearl10b.html. Reissued by PMLR on 04 October 2026.

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