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Data-driven sensitivity analysis for matching estimators

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  • Cerulli, Giovanni

Abstract

This paper proposes a sensitivity analysis test of unobservable selection for matching estimators based on a “leave-one-covariate-out” (LOCO) algorithm. Rooted in the machine learning literature, this sensitivity test performs a bootstrap over different subsets of covariates, and simulates various estimation scenarios to be compared with the baseline matching results. We provide an empirical application, comparing results with more traditional sensitivity tests.

Suggested Citation

  • Cerulli, Giovanni, 2019. "Data-driven sensitivity analysis for matching estimators," Economics Letters, Elsevier, vol. 185(C).
  • Handle: RePEc:eee:ecolet:v:185:y:2019:i:c:s0165176519303763
    DOI: 10.1016/j.econlet.2019.108749
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    References listed on IDEAS

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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    4. Giovanni Cerulli, 2015. "Econometric Evaluation of Socio-Economic Programs," Advanced Studies in Theoretical and Applied Econometrics, Springer, edition 127, number 978-3-662-46405-2, enero-jun.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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    More about this item

    Keywords

    Sensitivity analysis; Average treatment effects; Matching; Causal inference; Machine learning;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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