On the validity of covariate adjustment for estimating causal effects

Ilya Shpitser, Tyler VanderWeele, James M. Robins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Identifying effects of actions (treatments) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference. This identification is made difficult by the presence of confounders which can be related to both treatment and outcome variables. Confounders are often handled, both in theory and in practice, by adjusting for covariates, in other words considering outcomes conditioned on treatment and covariate values, weighed by probability of observing those covariate values. In this paper, we give a complete graphical criterion for covariate adjustment, which we term the adjustment criterion, and derive some interesting corollaries of the completeness of this criterion.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Pages527-536
Number of pages10
StatePublished - Dec 1 2010
Event26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 - Catalina Island, CA, United States
Duration: Jul 8 2010Jul 11 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Other

Other26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
CountryUnited States
CityCatalina Island, CA
Period7/8/107/11/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Shpitser, I., VanderWeele, T., & Robins, J. M. (2010). On the validity of covariate adjustment for estimating causal effects. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 (pp. 527-536). (Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010).