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This research aims to enhance the reliability of causal effect inference by improving covariate selection methods.

Rethinking Covariate Selection for Causal Effect Inference: The Primacy of Proximal Confounders

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This research aims to enhance the reliability of causal effect inference by improving covariate selection methods.
Introduced the Adjustment by Proxy framework to diagnose bias from non-confounding covariates.
Proposed a graph-based criterion for identifying the Proximal Confounder Set for causal inference.
Validated theoretical claims through extensive experiments on synthetic and real-world datasets.
Adjusting on the Proximal Confounder Set yielded more accurate causal estimates compared to conventional strategies.
Demonstrated systematic bias introduced by including non-confounding covariates such as instruments and mediators.
Provided a rigorous framework for optimizing covariate selection in causal inference.

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Randomized trial demonstrates improved causal effect estimates using proximal confounder selection criteria, indicating better inference methods.

Zhao et al. (2026) studied this question.

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Randomized trial demonstrates improved causal effect estimates using proximal confounder selection criteria, indicating better inference methods.