Skip to main content
SpringerLink
Log in

Matching Estimators

  • Reference work entry
  • First Online:
Book cover The New Palgrave Dictionary of Economics

  • 53 Accesses

Abstract

Matching methods are a popular method for evaluating the effects of programme or other treatment interventions. This article reviews recent developments in the econometric literature on matching estimators, including the assumptions required to justify their application, different ways of implementing the estimators and some recent empirical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 6,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 8,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Abadie, A. and G. Imbens 2006a. On the failure of the bootstrap for matching estimators. Technical working paper no. 325. Cambridge, MA: NBER.

    Google Scholar 

  • Abadie, A., and G. Imbens. 2006b. Large sample properties of matching estimators for average treatment effects. Econometrica 74: 235–267.

    Article  Google Scholar 

  • Angrist, J., and V. Lavy. 2001. Does teacher training affect pupil learning? evidence from matched comparisons in jerusalem public schools. Journal of Labor Economics 19: 343–369.

    Article  Google Scholar 

  • Behrman, J., Y. Cheng, and P. Todd. 2004. Evaluating preschool programs when length of exposure to the program varies: A nonparametric approach. The Review of Economics and Statistics 86: 108–132.

    Article  Google Scholar 

  • Chen, S. and M. Ravallion 2003. Hidden impact? ex-post evaluation of an antipoverty program. Policy Research Working paper no. 3049. Washington, DC: World Bank.

    Google Scholar 

  • Cochran, W., and D. Rubin. 1973. Controlling bias in observational studies. Sankyha 35: 417–446.

    Google Scholar 

  • Dehejia, R., and S. Wahba. 1999. Causal effects in non-experimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053–1062.

    Article  Google Scholar 

  • Dehejia, R., and S. Wahba. 2002. Propensity score matching methods for nonexperimental causal studies. The Review of Economics and Statistics 84: 151–161.

    Article  Google Scholar 

  • Diamond, A. and J.S. Sekhon 2005. Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. Working paper, Department of Political Science, Berkeley.

    Google Scholar 

  • Efron, B., and R. Tibshirani. 1993. An introduction to the bootstrap. New York: Chapman and Hall.

    Book  Google Scholar 

  • Eichler, M., and M. Lechner. 2002. An evaluation of public employment programmes in the East German state of Sachsen-Anhalt. Labour Economics 9: 143–186.

    Article  Google Scholar 

  • Fan, J. 1992a. Design adaptive nonparametric regression. Journal of the American Statistical Association 87: 998–1004.

    Article  Google Scholar 

  • Fan, J. 1992b. Local linear regression smoothers and their minimax efficiencies. Annals of Statistics 21: 196–216.

    Article  Google Scholar 

  • Fisher, R.A. 1935. Design of experiments. New York: Hafner.

    Google Scholar 

  • Friedlander, D., and P. Robins. 1995. Evaluating program evaluations: New evidence on commonly used nonexperimental methods. American Economic Review 85: 923–937.

    Google Scholar 

  • Galiani, S., P. Gertler, and E. Schargrodsky. 2005. Water for life: The impact of the privatization of water services on child mortality in argentina. Journal of Political Economy 113: 83–120.

    Article  Google Scholar 

  • Gertler, P., D. Levine, and M. Ames. 2004. Schooling and parental death. The Review of Economics and Statistics 86: 211–225.

    Article  Google Scholar 

  • Hahn, J. 1998. On the role of the propensity score in efficient estimation of average treatment effects. Econometrica 66: 315–331.

    Article  Google Scholar 

  • Heckman, J. and P. Todd 1995. Adapting propensity score matching and selection models to choice-based samples. Manuscript, Department of Economics, University of Chicago.

    Google Scholar 

  • Heckman, J., H. Ichimura, J. Smith, and P. Todd. 1996. Sources of selection bias in evaluating social programs: An interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method. Proceedings of the National Academy of Sciences 93: 13416–13420.

    Article  Google Scholar 

  • Heckman, J., J. Smith, and N. Clements. 1997a. Making the most out of social experiments: Accounting for heterogeneity in programme impacts. Review of Economic Studies 64: 487–536.

    Article  Google Scholar 

  • Heckman, J., H. Ichimura, and P. Todd. 1997b. Matching as an econometric evaluation estimator: Evidence from evaluating a job training program. Review of Economic Studies 64: 605–654.

    Article  Google Scholar 

  • Heckman, J., H. Ichimura, J. Smith, and P. Todd. 1998a. Characterizing selection bias using experimental data. Econometrica 66: 1017–1098.

    Article  Google Scholar 

  • Heckman, J., H. Ichimura, and P. Todd. 1998b. Matching as an econometric evaluation estimator. Review of Economic Studies 65: 261–294.

    Article  Google Scholar 

  • Heckman, J., R. Lalonde, and J. Smith. 1999. The economics and econometrics of active labor market programs. In Handbook of labor economics, ed. O. Ashenfelter and D. Card, Vol. 3A. Amsterdam: North-Holland.

    Google Scholar 

  • Hirano, K., and G. Imbens. 2004. The propensity score with continuous treatments. In Applied bayesian modeling and causal inference from incomplete data perspectives, ed. A. Gelman and X.L. Meng. New York: Wiley.

    Google Scholar 

  • Hirano, K., G. Imbens, and G. Ridder. 2003. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71: 1161–1189.

    Article  Google Scholar 

  • Holland, P.W. 1986. Statistics and causal inference (with discussion). Journal of the American Statistical Association 81: 945–970.

    Article  Google Scholar 

  • Horowitz, J.L. 1992. A smoothed maximum score estimator for the binary response model. Econometrica 60: 505–532.

    Article  Google Scholar 

  • Horowitz, J.L. 2003. The bootstrap. In Handbook of econometrics, ed. J.J. Heckman and E.E. Leamer, Vol. 5. Amsterdam: North-Holland.

    Google Scholar 

  • Ichimura, H. 1993. Semiparametric least squares and weighted SLS estimation of single index models. Journal of Econometrics 58: 71–120.

    Article  Google Scholar 

  • Imbens, G. 2000. The role of the propensity score in estimating dose-response functions. Biometrika 87: 706–710.

    Article  Google Scholar 

  • Jalan, J. and M. Ravallion 1999. Efficient estimation of average treatment effects: Evidence for argentina’s trabajar program. Policy research working paper. Washington, DC: World Bank.

    Google Scholar 

  • Jalan, J., and M. Ravallion. 2003. Does piped water reduce diarrhea for children in rural India. Journal of Econometrics 112: 153–173.

    Article  Google Scholar 

  • Klein, R.W., and R.H. Spady. 1993. An efficient semiparametric estimator for binary response models. Econometrica 61: 387–422.

    Article  Google Scholar 

  • LaLonde, R. 1986. Evaluating the econometric evaluations of training programs with experimental data. American Economic Review 76: 604–620.

    Google Scholar 

  • Lavy, V. 2002. Evaluating the effects of teachers’ group performance incentives on pupil achievement. Journal of Political Economy 110: 1286–1387.

    Article  Google Scholar 

  • Lavy, V. 2004. Performance pay and teachers’ effort, productivity and grading ethics. Working paper no. 10622. Cambridge, MA: NBER.

    Google Scholar 

  • Lechner, M. 2001. Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In Econometric evaluations of active labor market policies in Europe, ed. M. Lechner and F. Pfeiffer. Heidelberg: Physica.

    Chapter  Google Scholar 

  • Manski, C. 1973. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.

    Article  Google Scholar 

  • Manski, C., and S. Lerman. 1977. The estimation of choice probabilities from choice-based samples. Econometrica 45: 1977–1988.

    Article  Google Scholar 

  • Robinson, P. 1988. Root-N consistent nonparametric regression. Econometrica 56: 931–954.

    Article  Google Scholar 

  • Rosenbaum, P., and D. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41–55.

    Article  Google Scholar 

  • Rosenbaum, P., and D. Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician 39: 33–38.

    Google Scholar 

  • Rubin, D.B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66: 688–701.

    Article  Google Scholar 

  • Silverman, B.W. 1986. Density estimation for statistics and data analysis. London: Chapman and Hall.

    Book  Google Scholar 

  • Smith, J., and P. Todd. 2005. Does matching overcome lalonde’s critique of nonexperimental estimators? Journal of Econometrics 125: 305–353.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. http://link.springer.com/referencework/10.1057/978-1-349-95121-5

    Petra E. Todd

Authors
  1. Petra E. Todd
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Copyright information

© 2018 Macmillan Publishers Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Todd, P.E. (2018). Matching Estimators. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2104

Download citation

Publish with us

Policies and ethics

Search

Navigation