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Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method

Daniel Albalate (), Germà Bel () and Ferran A. Mazaira-Font ()
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Daniel Albalate: Department of Econometrics, Statistics and Applied Economics (Public Policy Unit). Universitat de Barcelona. John Keynes 1-11, 08034 Barcelona. Spain
Ferran A. Mazaira-Font: Department of Econometrics, Statistics and Applied Economics (Public Policy Unit). Universitat de Barcelona. John Keynes 1-11, 08034 Barcelona. Spain

No 202005, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

Abstract: The synthetic control method (SCM) has been increasingly adopted to evaluate causal effects under quasi-experimental designs. However, SCM suffers from sound weaknesses that compromise its accuracy, stability and meaningfulness. The SHAP-distance synthetic control method (SD-SCM) is proposed as solution. We evaluate the economic effect of the government formation deadlock in Spain, 2016. The deadlock did not negatively affect economic growth, as the economy grew 1.59% more without full government; standard SCM method overestimates the effect by 0.23 pp. We show that SD-SCM offers higher accuracy and stability, while ensuring the economic meaningfulness of covariates used in building the counterfactual.

Keywords: Synthetic control; SHAP; Regularization; Quasiexperiments; Causality; Government. JEL classification: C32; E65; H11. (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020-04, Revised 2020-04
New Economics Papers: this item is included in nep-gen and nep-ore
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