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Computing Synthetic Controls Using Bilevel Optimization

Author

Listed:
  • Pekka Malo

    (Aalto University School of Business)

  • Juha Eskelinen

    (Aalto University School of Business)

  • Xun Zhou

    (University of York)

  • Timo Kuosmanen

    (University of Turku)

Abstract

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush–Kuhn–Tucker approximations.

Suggested Citation

  • Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10471-7
    DOI: 10.1007/s10614-023-10471-7
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    More about this item

    Keywords

    Causal effects; Comparative case studies; Policy impact assessment; Bilevel optimization;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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