IDEAS home Printed from https://ideas.repec.org/p/boc/usug13/02.html
   My bibliography  Save this paper

treatrew: A user-written Stata routine for estimating average treatment effects by reweighting on propensity score

Author

Listed:
  • Giovanni Cerulli

    (Institute for Economic Research on Firms and Growth, Rome)

Abstract

Reweighting is a popular statistical technique to deal with inference in presence of a nonrandom sample. In the literature, various reweighting estimators have been proposed. This paper presents the author-written Stata routine treatrew, which implements the reweighting on the propensity-score estimator as proposed by Rosenbaum and Rubin (1983) in their seminal article, where they show that parameters’ standard errors can be obtained analytically (Wooldridge 2010, 920–930) or via bootstrapping. Because an implementation in Stata of this estimator with analytic standard errors was still missing, this paper, and the ado-file and help-file accompanying it, aims at filling this gap by providing an easy-to-use implementation of the reweighting on the propensity-score method, as a valuable tool for estimating treatment-effects under “selection-on-observables†(or “overt bias†). Finally, a Monte Carlo experiment to check the reliability of treatrew and to compare its results with other treatment effect estimators will also be provided.

Suggested Citation

  • Giovanni Cerulli, 2013. "treatrew: A user-written Stata routine for estimating average treatment effects by reweighting on propensity score," United Kingdom Stata Users' Group Meetings 2013 02, Stata Users Group.
    • Handle: RePEc:boc:usug13:02
    as

    Download full text from publisher

    File URL: http://repec.org/usug2013/cerulli.uk13.pdf
    File Function: presentation materials
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giovanni Cerulli, 2012. "contreatreg: A Stata module for estimating dose response treatment models under (continuous) treatment endogeneity and heterogeneous response to observable confounders," Italian Stata Users' Group Meetings 2012 03, Stata Users Group.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Brunell, Thomas L. & DiNardo, John, 2004. "A Propensity Score Reweighting Approach to Estimating the Partisan Effects of Full Turnout in American Presidential Elections," Political Analysis, Cambridge University Press, vol. 12(1), pages 28-45, January.
    5. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
    6. Li, Qi & Racine, Jeffrey S. & Wooldridge, Jeffrey M., 2009. "Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 206-223.
    7. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    8. Austin Nichols, 2007. "Causal inference with observational data," Stata Journal, StataCorp LP, vol. 7(4), pages 507-541, December.
    9. Giovanni Cerulli, 2012. "Ivtreatreg: a new STATA routine for estimating binary treatment models with heterogeneous response to treatment under observable and unobservable selection," CERIS Working Paper 201203, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    Full references (including those not matched with items on IDEAS)

    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. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
    2. Jain, Apoorva & Peter, Klara Sabirianova, 2017. "Limits to Wage Growth: Understanding the Wage Divergence between Immigrants and Natives," IZA Discussion Papers 10891, Institute of Labor Economics (IZA).
    3. Villalobos Barría, Carlos & Klasen, Stephan, 2016. "The impact of SENAI's vocational training program on employment, wages, and mobility in Brazil: Lessons for Sub Saharan Africa?," The Quarterly Review of Economics and Finance, Elsevier, vol. 62(C), pages 74-96.
    4. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    5. Dehejia Rajeev, 2015. "Experimental and Non-Experimental Methods in Development Economics: A Porous Dialectic," Journal of Globalization and Development, De Gruyter, vol. 6(1), pages 47-69, June.
    6. Graham, Bryan S. & Pinto, Cristine Campos de Xavier, 2022. "Semiparametrically efficient estimation of the average linear regression function," Journal of Econometrics, Elsevier, vol. 226(1), pages 115-138.
    7. Òscar Jordà & Alan M. Taylor, 2016. "The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy," Economic Journal, Royal Economic Society, vol. 126(590), pages 219-255, February.
    8. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    10. Kashi Kafle & Tisorn Songsermsawas & Paul Winters, 2022. "Agricultural value chain development in Nepal: Understanding mechanisms for poverty reduction," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 356-373, May.
    11. Rubolino, Enrico, 2019. "The efficiency and distributive effects of local taxes: evidence from Italian municipalities," ISER Working Paper Series 2019-02, Institute for Social and Economic Research.
    12. Meyer, Birgit, 2020. "How deep is your love? Innovation, Upgrading and the Depth of Internationalization," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224584, Verein für Socialpolitik / German Economic Association.
    13. Hung-Hao Chang, 2013. "Old Farmer Pension Program and Farm Succession: Evidence from a Population-Based Survey of Farm Households in Taiwan," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(4), pages 976-991.
    14. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    15. Arraiz, Irani & Calero, Carla & Jon, Songqing & Peralta, Alexandra, 2015. "Planting the seeds: The impact of training on mando producers in Haiti," 2015 Conference, August 9-14, 2015, Milan, Italy 212622, International Association of Agricultural Economists.
    16. Andrea Morescalchi, 2021. "A new career in a new town. Job search methods and regional mobility of unemployed workers," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 20(2), pages 223-272, May.
    17. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    18. Di Novi, Cinzia & Paruolo, Paolo & Verzillo, Stefano, 2023. "Does labour protection influence mental-health responses to employment shocks? Evidence on older workers in Europe," Economic Modelling, Elsevier, vol. 126(C).
    19. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    20. Garbero, A., 2016. "IFAD RESEARCH SERIES 7 - Measuring IFAD’s impact: background paper to the IFAD9 Impact Assessment Initiative," IFAD Research Series 280045, International Fund for Agricultural Development (IFAD).

    More about this item

    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:boc:usug13:02. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    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.