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Double-Robust Identification for Causal Panel Data Models
\title{\textbf{Double-Robust Identification for Causal Panel Data Models}\thanks{{\small This paper benefited greatly from our discussions with Manuel Arellano, St\'{e}phane Bonhomme, and David Hirshberg. We are grateful for comments from seminar participants at CERGE-EI, University of Chicago, University of Georgia, Princeton University, and various conferences.
This research was generously supported
by ONR grant N00014-17-1-2131. }} }
\author{Dmitry Arkhangelsky \thanks{{\small Associate Professor, CEMFI, [email removed]. }} \and Guido W. Imbens\thanks{{\small Professor of
Economics,
Graduate School of Business and Department of Economics, Stanford University, SIEPR, and NBER,
[email removed].}} }
\date{\ifcase\month\or
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July\or August\or September\or October\or November\or December\fi \ \number
\year\ \ (First version September 2019)}
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\begin{abstract}
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\noindent 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 observed and unobserved confounders. We focus on a different, complementary,
approach to identification where assumptions are made about the relation between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings but have rarely been 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.
\end{abstract}
\noindent \textbf{Keywords}: fixed effects, cross-section data, clustering, causal effects, treatment effects, unconfoundedness.
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