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...
| Published in: | Econometrica Vol. 93; no. 4; pp. 1165 - 1171 |
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| Main Authors: | , |
| Format: | Article |
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Wiley-Blackwell
Jul2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187056702&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187056702 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Jul2025 vid: 93 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 187056702 10.3982/ECTA22261 ppf: 1165 ppct: 6 formats: tig: atl: 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 aug: au: Imai, Kosuke Li, Michael Lingzhi affil: Department of Government and Department of Statistics, Harvard University Technology and Operations Management, Harvard Business School su: India Randomized controlled trials Machine learning Treatment effect heterogeneity Inferential statistics Optimization algorithms Immunology Ensemble learning Randomization (Statistics) sug: subj: Randomized controlled trials India Machine learning Treatment effect heterogeneity Inferential statistics Optimization algorithms Immunology Ensemble learning Randomization (Statistics) keyword: confidence intervals GATES Heterogeneous treatment effects machine learning randomization inference confidence intervals GATES Heterogeneous treatment effects machine learning randomization inference ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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