Matrix Completion Methods for Causal Panel Data Models

Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens and Khashayar Khosravi

No 25132, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the “missing” elements of the matrix, corresponding to treated units/periods. The approach estimates a matrix that well-approximates the original (incomplete) matrix, but has lower complexity according to the nuclear norm for matrices. From a technical perspective, we generalize results from the matrix completion literature by allowing the patterns of missing data to have a time series dependency structure. We also present novel insights concerning the connections between the matrix completion literature, the literature on interactive fixed effects models and the literatures on program evaluation under unconfoundedness and synthetic control methods.

JEL-codes: C01 C21 C23 (search for similar items in EconPapers)
Date: 2018-10
Note: IO LS TWP
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Citations: View citations in EconPapers (42)

Published as Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, vol 116(536), pages 1716-1730.

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Working Paper: Matrix Completion Methods for Causal Panel Data Models (2022)
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