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A Matching Estimator Based on a Bilevel Optimization Problem

Author

Listed:
  • Juan Díaz

    (Universidad de Chile)

  • Tomás Rau

    (Pontificia Universidad Católica de Chile)

  • Jorge Rivera

    (Universidad de Chile)

Abstract

This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balancing criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching or with minimized distance between them. We implement the proposed estimator with data from the National Supported Work Demonstration, finding outstanding performance in terms of covariate balance. Monte Carlo evidence shows that our estimator performs well in designs previously used in the literature.

Suggested Citation

  • Juan Díaz & Tomás Rau & Jorge Rivera, 2015. "A Matching Estimator Based on a Bilevel Optimization Problem," The Review of Economics and Statistics, MIT Press, vol. 97(4), pages 803-812, October.
    • Handle: RePEc:tpr:restat:v:97:y:2015:i:4:p:803-812
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    Cited by:

    1. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    2. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    3. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
    4. Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
    5. Wang, Yewen & Tang, Jiaxuan & Li, Cheng, 2025. "Registration reform and stock mispricing: Causal inference based on double machine learning," Research in International Business and Finance, Elsevier, vol. 73(PB).
    6. Wei Tian, 2023. "The Synthetic Control Method with Nonlinear Outcomes: Estimating the Impact of the 2019 Anti-Extradition Law Amendments Bill Protests on Hong Kong's Economy," Papers 2306.01967, arXiv.org.

    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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