On the Validity of Covariate Adjustment for Estimating Causal Effects
arXiv:1203.3515
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.
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)
References in corpus (1)
Cited by in corpus (21)
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- Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
- Identification of Causal Diffusion Effects Under Structural Stationarity
- Evaluating Causal Models by Comparing Interventional Distributions
- Efficient least squares for estimating total effects under linearity and causal sufficiency
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- From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
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- Practically Effective Adjustment Variable Selection in Causal Inference
- Finding and Listing Front-door Adjustment Sets
- Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
- Estimating Sibling Spillover Effects with Unobserved Confounding Using Gain-Scores
- A Robustness Test for Estimating Total Effects with Covariate Adjustment