367 citations · 433 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Distributionally Robust Causal Inference with Observational Data
Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is…
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
Kosuke Imai, Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fai…