NeurIPS 2021poster56 citations
Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu, Matt Kusner, Ricardo Silva
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.
Causal InferenceCausal Effects
BibTeX
@inproceedings{
kaddour2021causal,
title={Causal Effect Inference for Structured Treatments},
author={Jean Kaddour and Yuchen Zhu and Qi Liu and Matt Kusner and Ricardo Silva},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=0v9EPJGc10}
}