{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:52:58Z","timestamp":1791852778491,"version":"4.3.4"},"reference-count":92,"publisher":"The Econometric Society","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011061","name":"Agence Fran\u00e7aise de D\u00e9veloppement","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011061","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ECTA"],"abstract":"<jats:p>\n                    We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include\n                    <jats:italic>best linear predictors of the effects<\/jats:italic>\n                    using machine learning proxies,\n                    <jats:italic>average effects sorted by impact groups<\/jats:italic>\n                    , and\n                    <jats:italic>average characteristics of most and least impacted units<\/jats:italic>\n                    . The approach is valid in high\u2010dimensional settings, where the effects are proxied (but not necessarily consistently estimated) by predictive and causal machine learning methods. We post\u2010process these proxies into estimates of the key features. Our approach is generic; it can be used in conjunction with penalized methods, neural networks, random forests, boosted trees, and ensemble methods, both predictive and causal. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. We use quantile aggregation of the results across many potential splits, in particular taking medians of\n                    <jats:italic>p<\/jats:italic>\n                    \u2010values and medians and other quantiles of confidence intervals. We show that quantile aggregation lowers estimation risks over a single split procedure, and establish its principal inferential properties. Finally, our analysis reveals ways to build provably better machine learning proxies through causal learning: we can use the objective functions that we develop to construct the best linear predictors of the effects, to obtain better machine learning proxies in the initial step. We illustrate the use of both inferential tools and causal learners with a randomized field experiment that evaluates a combination of nudges to stimulate demand for immunization in India.\n                  <\/jats:p>","DOI":"10.3982\/ecta19303","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T05:45:07Z","timestamp":1753335907000},"page":"1121-1164","source":"Crossref","is-referenced-by-count":36,"title":["Fisher\u2013Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India"],"prefix":"10.3982","volume":"93","author":[{"given":"Victor","family":"Chernozhukov","sequence":"first","affiliation":[{"name":"Department of Economics and Center for Statistics and Data Sciences, MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mert","family":"Demirer","sequence":"additional","affiliation":[{"name":"Sloan School of Management, MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Duflo","sequence":"additional","affiliation":[{"name":"Department of Economics, MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iv\u00e1n","family":"Fern\u00e1ndez-Val","sequence":"additional","affiliation":[{"name":"Department of Economics, Boston University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2359","published-online":{"date-parts":[[2025]]},"reference":[{"issue":"1","key":"ecta200800-bib-0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/0034-6527.00321","volume":"72","author":"Abadie","year":"2005","journal-title":"The Review of Economic Studies"},{"key":"ecta200800-bib-0002","unstructured":"Abadie, Alberto, Matthew M. 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