对“Fisher–Schultz 演讲: 随机实验中异质性处理效应的通用机器学习推断, 及其在印度免疫中的应用”的评论

A Comment on: “Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India” by Victor Chernozhukov, Mert Demirer, Esther Duflo, and Iván Fernández‐Val

中文摘要

我们考察 Chernozhukov、Demirer、Duflo 和 Fernandez-Val 提出的分样本稳健推断（SSRI）方法，该方法用于量化机器学习（ML）模型所产生的异质性处理效应估计中的不确定性。尽管 SSRI 恰当地考虑了由样本分割带来的额外变异性，但在复杂 ML 模型下其计算成本会变得过高。我们提出一种基于随机化推断（RI）的替代方法，它在保留 SSRI 广泛适用性的同时，消除了重复样本分割的需要。借助交叉拟合与基于设计的推断，RI 程序能在大幅降低计算负担的情况下生成有效的置信区间。模拟研究表明，RI 方法在保持 SSRI 统计效率的同时，可扩展到规模大得多的应用与更复杂的情境。

Abstract

We examine the split‐sample robust inference (SSRI) methodology introduced by Chernozhukov, Demirer, Duflo, and Fernandez‐Val for quantifying uncertainty in heterogeneous treatment effect estimates produced by machine learning (ML) models. Although SSRI properly accounts for the additional variability due to sample splitting, its computational cost becomes prohibitive with complex ML models. We propose an alternative approach based on randomization inference (RI) that preserves the broad applicability of SSRI while eliminating the need for repeated sample splitting. Leveraging cross‐fitting and design‐based inference, the RI procedure yields valid confidence intervals with substantially reduced computational burden. Simulation studies demonstrate that the RI method preserves the statistical efficiency of SSRI while scaling to much larger applications and more complex settings.