{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:52:25Z","timestamp":1783662745197,"version":"3.55.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,16]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available; (ii) we establish a hidden front-door criterion, criterion to allow for hidden mediators for which proxies are available; (iii) we show that the identification of a certain causal effect called the population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)\u2013(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and, thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.<\/jats:p>","DOI":"10.1093\/biomet\/asae037","type":"journal-article","created":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T13:41:32Z","timestamp":1720878092000},"source":"Crossref","is-referenced-by-count":7,"title":["Causal inference with hidden mediators"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0460-2231","authenticated-orcid":false,"given":"Amiremad","family":"Ghassami","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Boston University , 665 Commonwealth Avenue , Boston, Massachusetts 02215,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Stanford University , 350 Jane Stanford Way , Stanford, California 94305,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilya","family":"Shpitser","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University , 3400 North Charles Street , Baltimore, Maryland 21218,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Tchetgen Tchetgen","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, The Wharton School, University of Pennsylvania , 265 South 37th Street , Philadelphia, Pennsylvania 19104,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"2025012017220496800_asae037-B1","volume-title":"Efficient and Adaptive Estimation for Semiparametric Models","author":"Bickel","year":"1993"},{"key":"2025012017220496800_asae037-B2","doi-asserted-by":"crossref","first-page":"5633","DOI":"10.1016\/S1573-4412(07)06077-1","volume-title":"Handbook of Econometrics, vol","author":"Carrasco","year":"2007"},{"key":"2025012017220496800_asae037-B3","doi-asserted-by":"crossref","first-page":"C1","DOI":"10.1111\/ectj.12097","article-title":"Double\/debiased machine learning for treatment and structural parameters","volume":"21","author":"Chernozhukov","year":"2018","journal-title":"Economet. 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