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Data-driven sensitivity analysis for matching estimators

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  • Giovanni Cerulli

    (Research Institute on Sustainable Economic Growth, Rome)

Abstract

Matching is a popular estimator of the Average Treatment Effects (ATEs) within counterfactual observational studies. In recent years, however, many scholars have questioned the validity of this approach for causal inference, as its reliability draws heavily upon the so-called selection-on-observables assumption. When unobservable confounders are possibly at work, they say, it becomes hard to trust matching results, and the analyst should consider alternative methods suitable for tackling unobservable selection. Unfortunately, these alternatives require extra information that may be costly to obtain, or even not accessible. For this reason, some scholars have proposed matching sensitivity tests for the possible presence of unobservable selection. The literature sets out two methods: the Rosenbaum (1987) and the Ichino, Mealli, and Nannicini (2008) tests. Both are implemented in Stata. In this work, I propose a third and different sensitivity test for unobservable selection in Matching estimation based on a ‘leave-covariates-out’ (LCO) approach. Rooted in the machine learning literature, this sensitivity test recalls a bootstrap over different subsets of covariates and simulates various estimation scenarios to be compared with the baseline matching estimated by the analyst. Finally, I will present sensimatch, the Stata routine I developed to run this method, and provide some instructional applications on real datasets.

Suggested Citation

  • Giovanni Cerulli, 2018. "Data-driven sensitivity analysis for matching estimators," London Stata Conference 2018 02, Stata Users Group.
  • Handle: RePEc:boc:usug18:02
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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, 2022. "Econometric Evaluation of Socio-Economic Programs," Advanced Studies in Theoretical and Applied Econometrics, Springer, edition 2, number 978-3-662-65945-8.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
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    Cited by:

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    2. Cristian Mardones & Pablo Herreros, 2023. "Ex post evaluation of voluntary environmental policies on the energy intensity in Chilean firms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9111-9136, September.
    3. Hynes, S. & Ankamah-Yeboah, I. & O’Neill, S. & Needham, K. & Bich Xuan, B. & Armstrong, C., 2020. "Entropy balancing for causal effects in discrete choice analysis: The Blue Planet II effect," Working Papers 309500, National University of Ireland, Galway, Socio-Economic Marine Research Unit.

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    More about this item

    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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