Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation

TL;DR

This paper introduces a new deep learning approach for treatment effect estimation that emphasizes interpretability and bias reduction, utilizing energy distance balancing and neural additive models, validated on benchmark datasets.

Contribution

It proposes a novel objective function based on energy distance for unbiased ATE estimation and integrates neural additive models for interpretability.

Findings

Outperforms state-of-the-art methods on IHDP and ACIC datasets.

Provides a tighter bias upper bound for ATE estimation.

Enhances interpretability of deep treatment effect models.

Abstract

Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the Weighted Energy Distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC.