IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v185y2019ics0165176519303763.html
   My bibliography  Save this article

Data-driven sensitivity analysis for matching estimators

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176519303763
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2019.108749?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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, July-Dece.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cortés, D & Robayo, M. A., 2023. "Efecto de las tutorías sobre el rendimiento académico: evidencia de estudiantes de economía," Documentos de Trabajo 20781, Universidad del Rosario.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.
    3. 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.
    4. Roberto Esposti, 2022. "Non-Monetary Motivations Of Agroenvironmental Policies Adoption. A Causal Forest Approach," Working Papers 459, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    5. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    6. Caloffi, Annalisa & Freo, Marzia & Ghinoi, Stefano & Mariani, Marco & Rossi, Federica, 2022. "Assessing the effects of a deliberate policy mix: The case of technology and innovation advisory services and innovation vouchers," Research Policy, Elsevier, vol. 51(6).
    7. Art B. Owen & Hal Varian, 2018. "Optimizing the tie-breaker regression discontinuity design," Papers 1808.07563, arXiv.org, revised Jul 2020.
    8. Colnet Bénédicte & Josse Julie & Varoquaux Gaël & Scornet Erwan, 2022. "Causal effect on a target population: A sensitivity analysis to handle missing covariates," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 372-414, January.
    9. Siverskog, Jonathan & Henriksson, Martin, 2022. "The health cost of reducing hospital bed capacity," Social Science & Medicine, Elsevier, vol. 313(C).
    10. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    11. Boockmann Bernhard & Buch Claudia M. & Schnitzer Monika, 2014. "Evidenzbasierte Wirtschaftspolitik in Deutschland: Defizite und Potentiale," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 15(4), pages 307-323, December.
    12. Karel Janda & Oleg Kravtsov, 2022. "Regulatory Stress Tests and Bank Responses: Heterogeneous Treatment Effect in Dynamic Settings," International Journal of Central Banking, International Journal of Central Banking, vol. 18(2), pages 1-49, June.
    13. Brox, Enzo & Krieger, Tommy, 2022. "Birthplace diversity and team performance," Labour Economics, Elsevier, vol. 79(C).
    14. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
    15. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.
    16. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    17. Capuno, Joseph & Kraft, Aleli & O'Donnell, Owen, 2021. "Effectiveness of clinic-based cardiovascular disease prevention: A randomized encouragement design experiment in the Philippines," Social Science & Medicine, Elsevier, vol. 283(C).
    18. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    19. Schaubert, Marianna, 2018. "Behavioral Response of Non-Resident Parents to Child Support Obligations: Evidence From SOEP," EconStor Preprints 203675, ZBW - Leibniz Information Centre for Economics.
    20. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:185:y:2019:i:c:s0165176519303763. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.