Doubly Robust Proxy Causal Learning with Neural Mean Embeddings

ArXi:2605.09514v1 Announce Type: new Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning addresses this problem through bridge equations involving treatment- and outcome-inducing proxies, avoiding direct recovery of the latent confounder. Existing doubly robust proxy estimators combine outcome and treatment bridges, but typically rely on fixed kernels, sieves, or low-dimensional semiparametric models; existing neural proxy methods are flexible, but are largely single-bridge estimators.