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Efficient semiparametric estimation of multi-valued treatment effects under ignorability

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  • Cattaneo, Matias D.

Abstract

This paper studies the efficient estimation of a large class of multi-valued treatment effects as implicitly defined by a collection of possibly over-identified non-smooth moment conditions when the treatment assignment is assumed to be ignorable. Two estimators are introduced together with a set of sufficient conditions that ensure their -consistency, asymptotic normality and efficiency. Under mild assumptions, these conditions are satisfied for the Marginal Mean Treatment Effect and the Marginal Quantile Treatment Effect, estimands of particular importance for empirical applications. Previous results for average and quantile treatments effects are encompassed by the methods proposed here when the treatment is dichotomous. The results are illustrated by an empirical application studying the effect of maternal smoking intensity during pregnancy on birth weight, and a Monte Carlo experiment.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 155 (2010)
Issue (Month): 2 (April)
Pages: 138-154

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Handle: RePEc:eee:econom:v:155:y:2010:i:2:p:138-154

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Multi-valued treatment effects Unconfoundedness Semiparametric efficiency Efficient estimation;

References

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  1. Guido Imbens & Jeffrey Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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Citations

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Cited by:
  1. Garlick, Robert, 2014. "Academic peer effects with different group assignment policies : residential tracking versus random assignment," Policy Research Working Paper Series 6787, The World Bank.
  2. Carlos A. Flores & Oscar A. Mitnik, 2013. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1691-1707, December.
  3. Firpo, Sergio, 2010. "Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures," IZA Discussion Papers 4841, Institute for the Study of Labor (IZA).
  4. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
  5. Alexandre Belloni & Victor Chernozhukov & Iván Fernández-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP57/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Sloczynski, Tymon & Wooldridge, Jeffrey M., 2014. "A General Double Robustness Result for Estimating Average Treatment Effects," IZA Discussion Papers 8084, Institute for the Study of Labor (IZA).
  7. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric estimation and inference using doubly robust moment conditions," Textos para discussão 330, Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
  8. Jane Cooley Fruehwirth & Salvador Navarro & Yuya Takahashi, 2011. "How The Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects," Working Papers 2011-015, Human Capital and Economic Opportunity Working Group.
  9. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute for the Study of Labor (IZA).
  10. Lai, Peng & Wang, Qihua, 2014. "Semiparametric efficient estimation for partially linear single-index models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 33-50.
  11. Ot�vio Bartalotti, 2013. "GMM Efficiency and IPW Estimation for Nonsmooth Functions," Working Papers 1301, Tulane University, Department of Economics.
  12. Yu, Wen, 2011. "Semiparametric analysis in double-sampling designs via empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1302-1314, October.

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