Computing Synthetic Controls Using Bilevel Optimization

Pekka Malo; Juha Eskelinen; Xun Zhou; Timo Kuosmanen

doi:10.1007/s10614-023-10471-7

Computing Synthetic Controls Using Bilevel Optimization

Pekka Malo, Juha Eskelinen, Xun Zhou^*, Timo Kuosmanen

^*Corresponding author for this work

Environment and Geography

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	24
Journal	Computational Economics
Early online date	25 Sept 2023
DOIs	https://doi.org/10.1007/s10614-023-10471-7
Publication status	E-pub ahead of print - 25 Sept 2023

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10.1007/s10614-023-10471-7Licence: CC BY

s10614-023-10471-7
© The Author(s) 2023
Final published version, 1.76 MBLicence: CC BY

Cite this

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