Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
This work introduces a neural framework called Doubly Robust Proxy Causal Learning (DRPCL) to identify causal effects in observational studies with continuous or structured treatments. It combines two learned 'bridges'—one for the outcome and one for the treatment—into a doubly robust estimator. This method provides reliable estimates of population and heterogeneous dose-response functions, outperforming existing neural single-bridge methods.