{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:44:17Z","timestamp":1768945457279,"version":"3.49.0"},"reference-count":33,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Understanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric definition of interaction and facilitates both the discovery and efficient estimation of interaction effects in mixed exposures. Leveraging stochastic shift interventions and ensemble machine learning, InterXshift identifies and quantifies interactions through a model-independent target parameter, estimated using targeted maximum likelihood estimation (TMLE) and cross-validation. The approach contrasts expected outcomes from joint interventions against those from individual exposures, enabling the detection of synergistic and antagonistic interactions. Validation through simulations and application to the National Institute of Environmental Health Sciences (NIEHS) Mixtures Workshop data demonstrate InterXshift\u2019s efficacy in accurately identifying true interaction directions and consistently highlighting significant impacts. We apply our methodology to National Health and Nutrition Examination Survey (NHANES) data to understand the interaction effect (if any) of furan exposure on leukocyte telomere length. This method enhances the analysis of multi-exposure interactions within high-dimensional datasets, offering robust methodological improvements for elucidating complex exposure dynamics in environmental health research. Additionally, we provide an open-source implementation of InterXshift in the\n                    <jats:monospace>InterXshift<\/jats:monospace>\n                    R package, facilitating its adoption and application by the research community.\n                  <\/jats:p>","DOI":"10.1515\/jci-2024-0058","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T14:58:32Z","timestamp":1768661912000},"source":"Crossref","is-referenced-by-count":0,"title":["Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions"],"prefix":"10.1515","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5515-6307","authenticated-orcid":false,"given":"David B.","family":"McCoy","sequence":"first","affiliation":[{"name":"Division of Biostatistics , University of California , Berkeley , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Hubbard","sequence":"additional","affiliation":[{"name":"Division of Biostatistics , University of California , Berkeley , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"van der Laan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics , University of California , Berkeley , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4853-6130","authenticated-orcid":false,"given":"Alejandro","family":"Schuler","sequence":"additional","affiliation":[{"name":"Division of Biostatistics , University of California , Berkeley , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"2026012009000498059_j_jci-2024-0058_ref_001","doi-asserted-by":"crossref","unstructured":"Vansteelandt, S, VanderWeele, TJ, Tchetgen, EJ, Robins, JM. Multiply robust inference for statistical interactions. J\u00a0Am Stat Assoc 2008;103:1693\u2013704. https:\/\/doi.org\/10.1198\/016214508000001084.","DOI":"10.1198\/016214508000001084"},{"key":"2026012009000498059_j_jci-2024-0058_ref_002","doi-asserted-by":"crossref","unstructured":"VanderWeele, TJ, Richardson, TS. General theory for interactions in sufficient-cause models with dichotomous exposures. Ann Stat 2012;40:2128\u201361. https:\/\/doi.org\/10.1214\/12-aos1019.","DOI":"10.1214\/12-AOS1019"},{"key":"2026012009000498059_j_jci-2024-0058_ref_003","doi-asserted-by":"crossref","unstructured":"Bobb, JF, Valeri, L, Henn, BC, Christiani, DC, Wright, RO, Mazumdar, M, et al.. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. 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