Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation

TL;DR

This paper introduces a differentiable Pareto-smoothed weighting method for high-dimensional heterogeneous treatment effect estimation, improving robustness and accuracy over traditional inverse probability weighting techniques.

Contribution

It proposes a novel end-to-end differentiable weighting framework that replaces extreme weights with Pareto smoothing, enhancing stability and performance in treatment effect estimation.

Findings

Outperforms existing weighting methods in experiments

Effectively corrects extreme weight values

Improves estimation accuracy in high-dimensional settings

Abstract

There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but are predictive of potential outcomes. To avoid losing such predictive feature information, existing methods learn separate feature representations using inverse probability weighting (IPW). However, due to their numerically unstable IPW weights, these methods suffer from estimation bias under a finite sample setup. To develop a numerically robust estimator by weighted representation learning, we propose a differentiable Pareto-smoothed weighting framework that replaces extreme weight values in an end-to-end fashion. Our experimental results show that by effectively correcting the weight values, our proposed method outperforms the existing ones, including traditional weighting schemes. Our code is available at https://github.com/ychika/DPSW.