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Poster
Causal Effect Inference for Structured Treatments
Jean Kaddour · Yuchen Zhu · Qi Liu · Matt Kusner · Ricardo Silva

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ None #None
in Poster Session 1 »

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.

Keywords: Graph Learning · Causality

Author Information

Jean Kaddour (University College London)
Yuchen Zhu (University College London)
Qi Liu (University of Oxford)
Matt Kusner (University College London)
Ricardo Silva (University College London)

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