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

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  • 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
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    File URL: http://repec.org/usug2013/cerulli.uk13.pdf
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    References listed on IDEAS

    as
    1. 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, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    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. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    4. 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.
    5. 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.
    6. Austin Nichols, 2007. "Causal inference with observational data," Stata Journal, StataCorp LP, vol. 7(4), pages 507-541, December.
    7. 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.
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