{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T12:12:17Z","timestamp":1778069537935,"version":"3.51.4"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,13]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variation, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by leveraging control outcomes. Using causal interpretations, we derive nonparametric identifiability of direct effects via negative-control outcomes. By utilizing surrogate-control outcomes as an extension of negative-control outcomes, we develop semiparametric inference on projected direct-effect estimands, accounting for hidden mediators, confounders and moderators. These estimands remain statistically meaningful under model misspecification and in the presence of error-prone embeddings. We provide bias quantifications and finite-sample linear expansions with uniform concentration bounds. The proposed doubly robust estimators are consistent and efficient under minimal assumptions and potential misspecification, facilitating data-adaptive estimation using machine learning algorithms. We evaluate our approach with random forests through simulations and the analysis of single-cell CRISPR-perturbed datasets, which may contain potential unmeasured confounders.<\/jats:p>","DOI":"10.1093\/biomet\/asag004","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T12:25:12Z","timestamp":1768911912000},"source":"Crossref","is-referenced-by-count":0,"title":["Assumption-lean post-integrated inference with surrogate-control outcomes"],"prefix":"10.1093","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9683-4146","authenticated-orcid":false,"given":"Jin-Hong","family":"Du","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science & Institute of Data Science, The University of Hong Kong , Pokfulam Road, 999077 Hong Kong SAR,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn","family":"Roeder","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh, Pennsylvania 15213, \u00a0 roeder@andrew.cmu.edu","place":["U.S.A"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Larry","family":"Wasserman","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh, Pennsylvania 15213, \u00a0 larry@stat.cmu.edu","place":["U.S.A"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"2026050607162538000_asag004-B1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1198\/jasa.2009.ap08746","article-title":"Synthetic control methods for comparative case studies: estimating the effect of California\u2019s tobacco control program","volume":"105","author":"Abadie","year":"2010","journal-title":"J. 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