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stat.ME2025
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
Michael Lingzhi Li, Kosuke Imai
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most…
stat.ME2025
Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
Kosuke Imai, Michael Lingzhi Li
We analyze the split-sample robust inference (SSRI) methodology proposed by Chernozhukov, Demirer, Duflo, and Fernandez-Val (CDDF) for quantifying uncertainty in heterogeneous trea…
stat.ME2024
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
Michael Lingzhi Li, Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework…