Uncertainty in Artificial Intelligence
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On the Validity of Covariate Adjustment for Estimating Causal Effects
Ilya Shpitser, Tyler VanderWeele, James Robins
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.
Keywords:
Pages: 527-536
PS Link:
PDF Link: /papers/10/p527-shpitser.pdf
BibTex:
@INPROCEEDINGS{Shpitser10,
AUTHOR = "Ilya Shpitser and Tyler VanderWeele and James Robins",
TITLE = "On the Validity of Covariate Adjustment for Estimating Causal Effects",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "527--536"
}


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