Yi Wang

Proximal Causal Learning under Unmeasured Confounding

PCL-U is proposed, a framework that learns proxy variables directly from observed covariates and uses neural encoders to decompose covariates into treatment-inducing, outcome-inducing, and shared proxies, guided by minimax mutual information objectives and obtains causal estimates through a practical moment-based risk...