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Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose selective randomization inference as a general framework for analysing adaptive experiments. In a nutshell, our approach applies conditional postselection inference to randomization tests. By using directed acyclic graphs to describe the data generating process, we derive a selective randomization p-value that controls the selective type-I error. As inference only relies on the randomness in the treatment assignment, no modelling assumptions or independent and identically distributed data are needed. We elaborate on conditions that render the proposed p-value computable and provide rejection sampling and MCMC algorithms to find a Monte Carlo approximation. Moreover, this article shows how to estimate and construct confidence intervals for a homogeneous treatment effect. Lastly, we demonstrate our method and compare it with other randomization tests using synthetic and real-world data.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkag081","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:43:12Z","timestamp":1779882192000},"source":"Crossref","is-referenced-by-count":0,"title":["Selective randomization inference for adaptive experiments"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0724-4297","authenticated-orcid":false,"given":"Tobias","family":"Freidling","sequence":"first","affiliation":[{"name":"Department of Mathematics, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne , Lausanne ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9902-2768","authenticated-orcid":false,"given":"Qingyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Statistical Laboratory, DPMMS, University of Cambridge , Cambridge ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4863-1656","authenticated-orcid":false,"given":"Zijun","family":"Gao","sequence":"additional","affiliation":[{"name":"Marshall School of Business, University of Southern California , Los Angeles ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"issue":"3","key":"2026060812124702400_qkag081-B1","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/s13142-014-0265-0","article-title":"Introduction to SMART designs for the development of adaptive interventions: With application to weight loss research","volume":"4","author":"Almirall","year":"2014","journal-title":"Translational Behavioral Medicine"},{"issue":"1","key":"2026060812124702400_qkag081-B2","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1093\/qje\/qjad043","article-title":"Inference on winners","volume":"139","author":"Andrews","year":"2024","journal-title":"The Quarterly Journal of Economics"},{"issue":"302","key":"2026060812124702400_qkag081-B3","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1080\/01621459.1963.10500851","article-title":"Sequential medical trials","volume":"58","author":"Anscombe","year":"1963","journal-title":"Journal of the American Statistical Association"},{"key":"2026060812124702400_qkag081-B4","volume-title":"Sequential medical trials","author":"Armitage","year":"1960"},{"issue":"2","key":"2026060812124702400_qkag081-B5","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1093\/biomet\/asy072","article-title":"Randomization tests of causal effects under interference","volume":"106","author":"Basse","year":"2019","journal-title":"Biometrika"},{"issue":"4","key":"2026060812124702400_qkag081-B6","doi-asserted-by":"publisher","DOI":"10.1162\/99608f92.fc62b261","article-title":"Selective inference: The silent killer of replicability","volume":"2","author":"Benjamini","year":"2020","journal-title":"Harvard Data Science Review"},{"issue":"1","key":"2026060812124702400_qkag081-B7","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"Journal of the Royal Statistical Society. 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