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Computing Synthetic Controls Using Bilevel Optimization
Tekijät: Malo Pekka, Eskelinen Juha, Zhou Xun, Kuosmanen Timo
Kustantaja: Springer
Julkaisuvuosi: 2023
Journal: Computational Economics
Lehden akronyymi: COMPUT ECON
Sivujen määrä: 24
ISSN: 0927-7099
eISSN: 1572-9974
DOI: https://doi.org/10.1007/s10614-023-10471-7
Verkko-osoite: https://link.springer.com/article/10.1007/s10614-023-10471-7
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181479863
Tiivistelmä
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.
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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