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

Kan Chen (), Qishuo Yin () and Qi Long ()
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Kan Chen: University of Pennsylvania
Qishuo Yin: University of Pennsylvania
Qi Long: University of Pennsylvania

Statistics in Biosciences, 2025, vol. 17, issue 1, No 7, 132-150

Abstract: 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 the 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, as well as is examined through the study of the effect of smoking on the blood level of cadmium using NHANES.

Keywords: Average treatment effect; Deep learning models; Generalization error bound; Weighted energy distance (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12561-023-09394-6

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