Causal effect estimation is fundamental for decision-making in many domains, e.g., personalized medicine. Having access to observational data only, how can we infer causal effects of structured treatments, e.g., molecular graphs of drugs, images, texts, etc.? 1/4 #NeurIPS We propose the generalized Robinson decomposition (GRD) for such treatments: it isolates the causal estimands (reducing regularization bias), allows one to plug in arbitrary models for learning, and possesses a quasi-oracle error bound under mild assumptions. 2/4