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

Yoichi Chikahara, Kansei Ushiyama

Keywords: treatment effect estimation, Pareto smoothing

TL;DR: To achieve high performance in CATE estimation from high-dimensional data, we propose a differentiable weight correction framework that replaces extreme IPW weight values with the quantiles of generalized Pareto distribution in an end-to-end fashion.

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 [this https URL](https://github.com/ychika/DPSW).

List Of Authors: Chikahara, Yoichi and Ushiyama, Kansei

Code Url: https://github.com/ychika/DPSW

Submission Number: 86