  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.
