Causal Effect Inference for Structured Treatments
arXiv:2106.01939
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.
NeurIPS 2021 Camera-Ready submission
References in corpus (5)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Fast Graph Representation Learning with PyTorch Geometric
- Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho
- An Introduction to Proximal Causal Learning
- RealCause: Realistic Causal Inference Benchmarking