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Simple and Bias-Corrected Matching Estimators for Average Treatment Effects

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Author Info
Alberto Abadie
Guido W. Imbens

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Abstract

Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. In this article, we develop a new framework to analyze the properties of matching estimators and establish a number of new results. First, we show that matching estimators include a conditional bias term which may not vanish at a rate faster than root-N when more than one continuous variable is used for matching. As a result, matching estimators may not be root-N-consistent. Second, we show that even after removing the conditional bias, matching estimators with a fixed number of matches do not reach the semiparametric efficiency bound for average treatment effects, although the efficiency loss may be small. Third, we propose a bias-correction that removes the conditional bias asymptotically, making matching estimators root-N-consistent. Fourth, we provide a new estimator for the conditional variance that does not require consistent nonparametric estimation of unknown functions. We apply the bias-corrected matching estimators to the study of the effects of a labor market program previously analyzed by Lalonde (1986). We also carry out a small simulation study based on Lalonde's example where a simple implementation of the biascorrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias and root-mean-squared-error. Software for implementing the proposed estimators in STATA and Matlab is available from the authors on the web.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0283.

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Date of creation: Oct 2002
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Handle: RePEc:nbr:nberte:0283

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C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation

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  1. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    Other versions:
  2. Manski, C.F. & Sandefur, G.D. & Mclanahan, S. & Powers, D., 1990. "Alternative Estimates Of The Effect Of Family Stucture During Adolescence On Hight School Graduation," Working papers 90-31, Wisconsin Madison - Social Systems.
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    Other versions:
  5. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267. [Downloadable!] (restricted)
  6. repec:att:wimass:199217 is not listed on IDEAS
  7. Richard Blundell & Mónica Costa Dias, 2008. "Alternative Approaches to Evaluation in Empirical Microeconomics," CETE Discussion Papers 0805, Universidade do Porto, Faculdade de Economia do Porto. [Downloadable!]
    Other versions:
  8. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
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    Other versions:
  10. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Blackwell Publishing, vol. 65(2), pages 261-94, April. [Downloadable!] (restricted)
  11. Jeffrey A. Smith & Petra E. Todd, 2001. "Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods," American Economic Review, American Economic Association, vol. 91(2), pages 112-118, May. [Downloadable!] (restricted)
  12. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May. [Downloadable!] (restricted)
  13. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April. [Downloadable!] (restricted)
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  15. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  16. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  19. V. Joseph Hotz & Guido W. Imbens & Julie H. Mortimer, 1999. "Predicting the Efficacy of Future Training Programs Using Past Experiences," NBER Technical Working Papers 0238, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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    Other versions:
  22. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
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