Causal Effect Inference for Structured Treatments

Part of Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)

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Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva


We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e.g., graphs, images, texts). Given a weak condition on the effect, we propose the generalized Robinson decomposition, which (i) isolates the causal estimand (reducing regularization bias), (ii) allows one to plug in arbitrary models for learning, and (iii) possesses a quasi-oracle convergence guarantee under mild assumptions. In experiments with small-world and molecular graphs we demonstrate that our approach outperforms prior work in CATE estimation.