Computing Synthetic Controls Using Bilevel Optimization

Pekka Malo (), Juha Eskelinen (), Xun Zhou () and Timo Kuosmanen
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Pekka Malo: Aalto University School of Business
Juha Eskelinen: Aalto University School of Business
Xun Zhou: University of York

Computational Economics, 2024, vol. 64, issue 2, No 16, 1113-1136

Abstract: 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.

Keywords: Causal effects; Comparative case studies; Policy impact assessment; Bilevel optimization (search for similar items in EconPapers)
JEL-codes: C31 C54 C61 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Computing Synthetic Controls Using Bilevel Optimization (2020)
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DOI: 10.1007/s10614-023-10471-7

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