Papers › Causal Effect Inference for Structured Treatments
Causal Effect Inference for Structured Treatments
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.
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Syntology Ran 17 of 22 code samples harvested from 2 repositories linked to this paper; 5 have no recorded run. Of those that ran: 11 ran · our draft was wrong; 6 ran with no contract checked.
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