Estimating Conditional Average Treatment Effects via Sufficient Representation Learning

TL;DR

This paper introduces CrossNet, a neural network method that learns sufficient feature representations for more accurate conditional average treatment effect estimation, even with high-dimensional data, by leveraging cross-group data.

Contribution

The paper proposes a novel neural network approach, CrossNet, that ensures the learned representations satisfy unconfoundedness and improves CATE estimation accuracy.

Findings

Outperforms existing methods in simulations and empirical tests.

Effectively verifies unconfoundedness in high-dimensional settings.

Utilizes cross-group data to enhance regression estimates.

Abstract

Estimating the conditional average treatment effects (CATE) is very important in causal inference and has a wide range of applications across many fields. In the estimation process of CATE, the unconfoundedness assumption is typically required to ensure the identifiability of the regression problems. When estimating CATE using high-dimensional data, there have been many variable selection methods and neural network approaches based on representation learning, while these methods do not provide a way to verify whether the subset of variables after dimensionality reduction or the learned representations still satisfy the unconfoundedness assumption during the estimation process, which can lead to ineffective estimates of the treatment effects. Additionally, these methods typically use data from only the treatment or control group when estimating the regression functions for each group. This paper proposes a novel neural network approach named \textbf{CrossNet} to learn a sufficient representation for the features, based on which we then estimate the CATE, where cross indicates that in estimating the regression functions, we used data from their own group as well as cross-utilized data from another group. Numerical simulations and empirical results demonstrate that our method outperforms the competitive approaches.