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Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention\u2014induced modified treatment policies\u2014which we show identify such causal effects in the presence of network interference. Building on recent developments for causal inference in networks, we provide flexible, semi-parametric efficient estimators of the statistical estimand. Numerical experiments demonstrate that an induced modified treatment policy can eliminate the causal, or identification, bias that results from network interference. We use the methodology developed to evaluate the effect of zero-emission vehicle uptake on air pollution in California, strengthening prior evidence.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkag052","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T12:43:56Z","timestamp":1772023436000},"page":"1407-1426","source":"Crossref","is-referenced-by-count":1,"title":["The causal effects of modified treatment policies under network interference"],"prefix":"10.1093","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4695-833X","authenticated-orcid":false,"given":"Salvador V","family":"Balkus","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. 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