Doubly robust identification for causal panel data models

Sufficient statistics for unobserved heterogeneity in structural dynamic logit models

Dmitry Arkhangelsky and Guido W Imbens

The Econometrics Journal, 2022, vol. 25, issue 3, 649-674

Abstract: SummaryWe 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 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 settings, 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.

Keywords: Fixed effects; cross-section data; clustering; causal effects; treatment effects; unconfoundedness (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:25:y:2022:i:3:p:649-674.

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