Double-Robust Identification for Causal Panel Data Models

55 Pages Posted: 27 Jan 2021 Last revised: 18 Nov 2021

See all articles by Dmitry Arkhangelsky

Dmitry Arkhangelsky

Centre for Monetary and Financial Studies (CEMFI)

Guido W. Imbens

Stanford Graduate School of Business

Date Written: January 2021

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.

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Suggested Citation

Arkhangelsky, Dmitry and Imbens, Guido W., Double-Robust Identification for Causal Panel Data Models (January 2021). NBER Working Paper No. w28364, Available at SSRN: https://ssrn.com/abstract=3772603 or http://dx.doi.org/10.2139/ssrn.3772603

Dmitry Arkhangelsky (Contact Author)

Centre for Monetary and Financial Studies (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain

Guido W. Imbens

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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