Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness

The synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimental designs. However, SCM suffers from weaknesses that compromise its accuracy, stability and meaningfulness, due to the nested optimization problem of covariate relevance and counterfactual weights. We...

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Bibliographic Details
Published inSERIEs : journal of the Spanish Economic Association Vol. 12; no. 4; pp. 549 - 584
Main Authors Albalate, Daniel, Bel, Germà, Mazaira-Font, Ferran A
Format Journal Article
LanguageEnglish
Published Heidelberg Springer 01.12.2021
Springer Berlin Heidelberg
Springer Nature B.V
Subjects
Accuracy
Causality
Counterfactual thinking
Economic analysis
Economic Policy
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Government
Microeconomics
Optimization
Original Article
Public Finance
Quasi-experimental methods
Quasi-experiments ·Causality
Regularization
SHAP
Synthetic control
SHAP
Synthetic control
C32
Government
E65
H11
Quasi-experiments
Causality
Regularization
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Summary:The synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimental designs. However, SCM suffers from weaknesses that compromise its accuracy, stability and meaningfulness, due to the nested optimization problem of covariate relevance and counterfactual weights. We propose a decoupling of both problems. We evaluate the economic effect of government formation deadlock in Spain-2016 and find that SCM method overestimates the effect by 0.23 pp. Furthermore, we replicate two studies and compare results from standard and decoupled SCM. Decoupled SCM offers higher accuracy and stability, while ensuring the economic meaningfulness of covariates used in building the counterfactual.
ISSN:1869-4195
1869-4187
1869-4195
DOI:10.1007/s13209-021-00242-8