提出基于相对误差的评估框架，可可靠比较不同异质处理效应估计器性能。
A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
- 通过理论推导确定关键干扰参数条件，构建鲁棒相对误差评估机制。
- 设计新型损失函数与神经网络架构，实现对干扰参数的准确估计。
- 框架可用于模型比较，新算法融合历史估计器提升效果，适合因果推断研究者。
尽管异质处理效应（HTE）估计已取得显著进展，但其评估方法仍不完善。本文提出一种基于相对误差的稳健评估框架，量化两个HTE估计器之间的性能差异。我们首先推导出实现相对误差鲁棒估计所需的干扰参数关键理论条件，并基于此设计新的损失函数，构建神经网络架构以估计干扰参数并获得相对误差的稳健估计，从而实现对HTE估计器的可靠评估。本文还给出了所提相对误差估计器的大样本性质。此外，除了评估外，我们提出一种新的HTE学习算法，利用先前的估计器和通过神经网络学习到的干扰参数。大量实验表明，该评估框架能支持对HTE估计器的可靠比较，所提出的HTE学习算法表现出良好性能。
原文摘要 · Abstract (English)
While significant progress has been made in heterogeneous treatment effect (HTE) estimation, the evaluation of HTE estimators remains underdeveloped. In this article, we propose a robust evaluation framework based on relative error, which quantifies performance differences between two HTE estimators. We first derive the key theoretical conditions on the nuisance parameters that are necessary to achieve a robust estimator of relative error. Building on these conditions, we introduce novel loss functions and design a neural network architecture to estimate nuisance parameters and obtain robust estimation of relative error, thereby achieving reliable evaluation of HTE estimators. We provide the large sample properties of the proposed relative error estimator. Furthermore, beyond evaluation, we propose a new learning algorithm for HTE that leverages both the previously HTE estimators and the nuisance parameters learned through our neural network architecture. Extensive experiments demonstrate that our evaluation framework supports reliable comparisons across HTE estimators, and the proposed learning algorithm for HTE exhibits desirable performance.
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