What is it about?
Causal inference often assumes that controlling for more variables leads to better estimates. Our work shows that this is not always true: adjusting for the wrong variables can increase bias. We introduce the Proximal Confounder Set, a minimal set of confounders sufficient for reliable adjustment, and show on both simulated and real-world data that this targeted strategy can improve causal effect estimation.
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Why is it important?
Reliable causal inference depends on choosing the right variables to adjust for. Our study shows that unnecessary adjustment can increase bias and reduce accuracy. By identifying a minimal set of proximal confounders, our approach offers a simpler and more reliable way to estimate causal effects in complex data.
Perspectives
What motivated me most was a simple question: when does adjusting for more variables actually make causal inference worse? I hope this work encourages researchers to treat covariate selection not as a routine preprocessing step, but as a central part of causal reasoning.
Yonghe Zhao
Jilin University
Read the Original
This page is a summary of: Rethinking Covariate Selection for Causal Effect Inference: The Primacy of Proximal Confounders, ACM Transactions on Knowledge Discovery from Data, July 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3833418.
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