Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

TL;DR

This paper introduces FSRM, a deep learning-based matching method that improves treatment effect estimation by learning balanced, relevant representations of covariates, addressing high-dimensional data challenges in observational studies.

Contribution

The paper proposes a novel feature selection representation matching (FSRM) method using deep learning and Wasserstein regularization for better treatment effect estimation.

Findings

FSRM outperforms existing methods on three datasets.

Deep representation learning enhances matching accuracy.

Regularization ensures balanced covariate representations.

Abstract

The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. However, estimating treatment effects from observational data is faced with two major challenges, missing counterfactual outcomes and treatment selection bias. Matching methods are among the most widely used and fundamental approaches to estimating treatment effects, but existing matching methods have poor performance when facing data with high dimensional and complicated variables. We propose a feature selection representation matching (FSRM) method based on deep representation learning and matching, which maps the original covariate space into a selective, nonlinear, and balanced representation space, and then conducts matching in the learned representation space. FSRM adopts deep feature selection to minimize the influence of irrelevant variables for estimating treatment effects and incorporates a regularizer based on the Wasserstein distance to learn balanced representations. We evaluate the performance of our FSRM method on three datasets, and the results demonstrate superiority over the state-of-the-art methods.