Double-Robust Identification for Causal Panel Data Models

Dmitry Arkhangelsky and Guido Imbens

No 28364, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserved confounders. We focus on a novel, complementary, approach to identification where assumptions are made about the relation between the treatment assignment and the unobserved confounders. 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.

JEL-codes: C01 C1 C23 (search for similar items in EconPapers)
Date: 2021-01
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (5)

Published as The Econometrics Journal, Volume 25, Issue 3, September 2022, Pages 649–674.

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Working Paper: Doubly Robust Identification for Causal Panel Data Models (2022)
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