{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:05:10Z","timestamp":1754154310940,"version":"3.41.2"},"reference-count":6,"publisher":"The Econometric Society","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ECTA"],"abstract":"<jats:p>We examine the split\u2010sample robust inference (SSRI) methodology introduced by Chernozhukov, Demirer, Duflo, and Fernandez\u2010Val 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\u2010fitting and design\u2010based 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.<\/jats:p>","DOI":"10.3982\/ecta22261","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T05:45:07Z","timestamp":1753335907000},"page":"1165-1170","source":"Crossref","is-referenced-by-count":0,"title":["A Comment on: \n            <i>\u201cFisher\u2013Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India\u201d by Victor Chernozhukov, Mert Demirer, Esther Duflo, and Iv\u00e1n Fern\u00e1ndez\u2010Val<\/i>"],"prefix":"10.3982","volume":"93","author":[{"given":"Kosuke","family":"Imai","sequence":"first","affiliation":[{"name":"Department of Government and Department of Statistics, Harvard University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael Lingzhi","family":"Li","sequence":"additional","affiliation":[{"name":"Technology and Operations Management, Harvard Business School"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2359","published-online":{"date-parts":[[2025]]},"reference":[{"issue":"1","key":"ecta200802-bib-0001","first-page":"43","volume":"34","author":"Dorie","year":"2019","journal-title":"Statistical Science"},{"issue":"541","key":"ecta200802-bib-0002","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/01621459.2021.1923511","volume":"118","author":"Imai","year":"2023","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"ecta200802-bib-0003","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1080\/07350015.2024.2358909","volume":"43","author":"Imai","year":"2025","journal-title":"Journal of Business & Economic Statistics"},{"key":"ecta200802-bib-0004","unstructured":"Li, Michael Lingzhi, Kosuke Imai, Jialu Li, and Xiaolong Yang (2023): \u201cevalITR: Evaluating Individualized Treatment Rules,\u201d, https:\/\/CRAN.R-project.org\/package=evalITR. Version 1.0.0."},{"key":"ecta200802-bib-0005","first-page":"307","volume-title":"Advances in Neural Information Processing Systems","author":"Nadeau","year":"2000"},{"key":"ecta200802-bib-0006","first-page":"465","volume":"5","author":"Neyman","year":"1923","journal-title":"Statistical Science"}],"container-title":["Econometrica"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T05:45:27Z","timestamp":1753335927000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.econometricsociety.org\/doi\/10.3982\/ECTA22261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":6,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025]]}},"alternative-id":["10.3982\/ECTA22261"],"URL":"https:\/\/doi.org\/10.3982\/ecta22261","relation":{},"ISSN":["0012-9682"],"issn-type":[{"type":"print","value":"0012-9682"}],"subject":[],"published":{"date-parts":[[2025]]}}}