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

• URL: http://arxiv.org/abs/2203.03185v2
• Date: Tue, 8 Mar 2022 09:34:07 GMT
• Title: Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation
• Authors: Kan Chen, Qishuo Yin, Qi Long
• Abstract summary: We propose an upper bound for the bias of average treatment estimation under the strong ignorability assumption. We implement this upper bound as an objective function being minimized by leveraging a novel additive neural network architecture. The proposed method is illustrated by re-examining the benchmark datasets for causal inference, and it outperforms the state-of-art.
• Score: 15.465045049754336
• License: http://creativecommons.org/licenses/by/4.0/
• 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 give a theoretical analysis and propose an upper bound for the bias of average treatment effect estimation under the strong ignorability assumption. The proposed upper bound consists of two parts: training error for factual outcomes, and the distance between treated and control distributions. We use the Weighted Energy Distance (WED) to measure the distance between two distributions. Motivated by the theoretical analysis, we implement this upper bound as an objective function being minimized by leveraging a novel additive neural network architecture, which combines the expressivity of deep neural network, the interpretability of generalized additive model, the sufficiency of the balancing score for estimation adjustment, and covariate balancing properties of treated and control distributions, for estimating average treatment effects from observational data. Furthermore, we impose a so-called weighted regularization procedure based on non-parametric theory, to obtain some desirable asymptotic properties. The proposed method is illustrated by re-examining the benchmark datasets for causal inference, and it outperforms the state-of-art.

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