We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.

翻译：本文提出一种结合分区匹配策略的模型平均方法，用于估计异方差误差设定下的条件平均处理效应。该方法在权重与估计量层面兼具渐近最优性与一致性。数值模拟研究表明，所提方法在有限样本条件下表现出优良的统计性能。