Causal Effect Inference for Structured Treatments

Code

• github.com/jeankaddour/sin
  OfficialIn paperpytorch★ 42
• github.com/JeanKaddour/GIN
  OfficialIn paperpytorch★ 42

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.

Reproductions