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The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about \u03c8-network dependence. Finally, we provide a consistent variance estimator.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkad132","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T04:06:11Z","timestamp":1702699571000},"page":"487-511","source":"Crossref","is-referenced-by-count":4,"title":["Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding"],"prefix":"10.1093","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5491-2174","authenticated-orcid":false,"given":"Naoki","family":"Egami","sequence":"first","affiliation":[{"name":"Department of Political Science, Columbia University , New York, NY , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric J","family":"Tchetgen Tchetgen","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science and Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, PA , 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