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Synthetic Control Methods for Proportions

Author

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  • Bogatyrev, Konstantin

    (Bocconi University)

  • Stoetzer, Lukas

Abstract

Synthetic control methods are widely used for causal inference in case studies and panel data settings, often applied to model counterfactuals for proportional outcomes. However, conventional synthetic control methods are designed for univariate outcomes, leading researchers to model counterfactuals for each proportion separately. We propose an extension to synthetic control methods to simultaneously handle multivariate outcomes in compositional data. Our approach establishes constant control comparisons by using the same weights for each proportion, improving comparability while adhering to treatment constraints. We illustrate the benefits of our method through a simulation and an empirical application to a recent study on climate policy effects in the 2019 Spanish general election. This advancement extends the validity and reliability of synthetic control estimates to common outcomes in political science.

Suggested Citation

  • Bogatyrev, Konstantin & Stoetzer, Lukas, 2025. "Synthetic Control Methods for Proportions," OSF Preprints brhd3_v2, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:brhd3_v2
    DOI: 10.31219/osf.io/brhd3_v2
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    References listed on IDEAS

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