Causal Effect Inference for Structured TreatmentsDownload PDF

May 21, 2021 (edited Oct 26, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Causal Inference, Causal Effects
  • TL;DR: This paper addresses causal effect inference when treatments are structured (e.g., graphs, images, text, etc.) by generalizing the Robinson decomposition to isolate the causal estimand.
  • Abstract: 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.
  • Supplementary Material: pdf
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  • Code: https://github.com/JeanKaddour/SIN
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