无需真实处理效应即可可靠选择最优异质处理效应估计器。
Reliable Selection of Heterogeneous Treatment Effect Estimators
- 提出基于交叉拟合与加权检验的无真值选择方法
- 在多个基准数据集上显著降低误选率
- 适合处理效应不可观测的因果推断场景
我们研究在处理效应无法观测的场景下，从一组候选异质处理效应（HTE）估计器中选出最优者的问题。将估计器选择建模为多重假设检验问题，提出一种无需真值的交叉拟合、指数加权检验统计量方法。关键在于双向样本分割设计，分离了干扰项估计与权重学习，保障推断稳定性。基于稳定性中心极限定理，在较弱正则条件下实现了渐近族错误率控制。实证结果显示，该方法在ACIC 2016、IHDP和Twins基准上均表现出可靠的误差控制，并显著减少误选，证明其在无真值条件下仍具可行性和强大性能。
原文摘要 · Abstract (English)
We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where the treatment effect is fundamentally unobserved. We cast estimator selection as a multiple testing problem and introduce a ground-truth-free procedure based on a cross-fitted, exponentially weighted test statistic. A key component of our method is a two-way sample splitting scheme that decouples nuisance estimation from weight learning and ensures the stability required for valid inference. Leveraging a stability-based central limit theorem, we establish asymptotic familywise error rate control under mild regularity conditions. Empirically, our procedure provides reliable error control while substantially reducing false selections compared with commonly used methods across ACIC 2016, IHDP, and Twins benchmarks, demonstrating that our method is feasible and powerful even without ground-truth treatment effects.
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