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      Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

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      Journal of Economic Literature
      American Economic Association

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          Abstract

          Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research. (JEL B41, C32, C54, E23, F15, O47)

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          Most cited references52

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          Estimating causal effects of treatments in randomized and nonrandomized studies.

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            Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program

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              Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

              This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.
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                Author and article information

                Journal
                Journal of Economic Literature
                Journal of Economic Literature
                American Economic Association
                0022-0515
                June 01 2021
                June 01 2021
                : 59
                : 2
                : 391-425
                Affiliations
                [1 ] Department of Economics, Massachusetts Institute of Technology.
                Article
                10.1257/jel.20191450
                869a2403-760d-4e99-9fae-e7837451c8c2
                © 2021
                History

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