On Measurement Bias in Causal Inference

Judea Pearl
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:416-423, 2010.

Abstract

This paper addresses the problem of measure- ment errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias- free effect estimates in such models.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-pearl10a, title = {On Measurement Bias in Causal Inference}, author = {Pearl, Judea}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {416--423}, 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/pearl10a/pearl10a.pdf}, url = {https://proceedings.mlr.press/r8/pearl10a.html}, abstract = {This paper addresses the problem of measure- ment errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias- free effect estimates in such models.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T On Measurement Bias in Causal Inference %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-pearl10a %I PMLR %P 416--423 %U https://proceedings.mlr.press/r8/pearl10a.html %V R8 %X This paper addresses the problem of measure- ment errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias- free effect estimates in such models. %Z Reissued by PMLR on 04 October 2026.
APA
Pearl, J.. (2010). On Measurement Bias in Causal Inference. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:416-423 Available from https://proceedings.mlr.press/r8/pearl10a.html. Reissued by PMLR on 04 October 2026.

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