Treatment Effect Estimation for Exponential Family Outcomes using Neural Networks with Targeted Regularization

• URL: http://arxiv.org/abs/2502.07295v1
• Date: Tue, 11 Feb 2025 06:36:20 GMT
• Title: Treatment Effect Estimation for Exponential Family Outcomes using Neural Networks with Targeted Regularization
• Authors: Jiahong Li, Zeqin Yang, Jiayi Dan, Jixing Xu, Zhichao Zou, Peng Zhen, Jiecheng Guo,
• Abstract summary: We show how to design NN-based estimators with desirable properties, such as low bias and doubly robustness. We develop a NN-based estimator for ADCF by generalizing functional targeted regularization to exponential families, and provide the corresponding theoretical convergence rate.
• Score: 5.124955904159979
• License:
• Abstract: Neural Networks (NNs) have became a natural choice for treatment effect estimation due to their strong approximation capabilities. Nevertheless, how to design NN-based estimators with desirable properties, such as low bias and doubly robustness, still remains a significant challenge. A common approach to address this is targeted regularization, which modifies the objective function of NNs. However, existing works on targeted regularization are limited to Gaussian-distributed outcomes, significantly restricting their applicability in real-world scenarios. In this work, we aim to bridge this blank by extending this framework to the boarder exponential family outcomes. Specifically, we first derive the von-Mises expansion of the Average Dose function of Canonical Functions (ADCF), which inspires us how to construct a doubly robust estimator with good properties. Based on this, we develop a NN-based estimator for ADCF by generalizing functional targeted regularization to exponential families, and provide the corresponding theoretical convergence rate. Extensive experimental results demonstrate the effectiveness of our proposed model.

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