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Traditionally, researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification, where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section\u00a0settings, but rarely used with panel data. We introduce different sets of assumptions that follow the two paths to identification and develop a double robust approach. We propose estimation methods that build on these identification strategies.<\/jats:p>","DOI":"10.1093\/ectj\/utac019","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T20:23:45Z","timestamp":1656102225000},"page":"649-674","source":"Crossref","is-referenced-by-count":16,"title":["Doubly robust identification for causal panel data models"],"prefix":"10.1093","volume":"25","author":[{"given":"Dmitry","family":"Arkhangelsky","sequence":"first","affiliation":[{"name":"Associate Professor, CEMFI , Casado del Alisal, 5, 28014, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guido W","family":"Imbens","sequence":"additional","affiliation":[{"name":"Professor of Economics, Graduate School of Business and Department of Economics, Stanford University , 655 Knight Way, Stanford, CA 94305, United 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