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

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 variabili...

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Bibliographic Details

Published in:	Econometrica Vol. 93; no. 4; pp. 1165 - 1171
Main Authors:	Imai, Kosuke, Li, Michael Lingzhi
Format:	Article
Published:	Wiley-Blackwell Jul2025
Subjects:	India Randomized controlled trials Machine learning Treatment effect heterogeneity Inferential statistics Optimization algorithms Immunology Ensemble learning Randomization (Statistics)
Online Access:	View this record in EBSCOhost