Nguyen Viet, Cuong (2012): Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study.
Download (103Kb) | Preview
Propensity score matching is a widely-used method to measure the effect of a treatment in social as well as health sciences. An important issue in propensity score matching is how to select conditioning variables in estimation of the propensity score. It is commonly mentioned that only variables which affect both program participation and outcomes are selected. Using Monte Carlo simulation, this paper shows that efficiency in estimation of the Average Treatment Effect on the Treated can be gained if all the available observed variables in the outcome equation are included in the estimation of the propensity score.
|Item Type:||MPRA Paper|
|Original Title:||Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study|
|English Title:||Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study|
|Keywords:||Impact evaluation, treatment effect, propensity score matching, covariate selection, Monte Carlo|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
H - Public Economics > H4 - Publicly Provided Goods > H43 - Project Evaluation; Social Discount Rate
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General
|Depositing User:||Cuong Nguyen Viet|
|Date Deposited:||03. Feb 2012 11:46|
|Last Modified:||12. Feb 2013 11:56|
Abadie, A., and Imbens G. W. (2008), “On the Failure of the Bootstrap for Matching Estimators”, Econometrica, Vol. 76(6), 1537-1557.
Augurzky, B. and Schmidt, C.M. (2001), “The Propensity Score: a Means to an End”, IZA Discussion Paper Series No. 271.
Austin, P. C. (2007), “The Performance of Different Propensity Score Methods For Estimating Marginal Odds Ratios”, Statistics in Medicine, 26, 3078–3094.
Bryson, A., R. Dorsett, and S. Purdon (2002), “The Use of Propensity Score Matching in the Evaluation of Labour Market Policies," Working Paper No. 4, Department for Work and Pensions.
Caliendo, M. and S. Kopeinig (2008), “Some Practical Guidance for the Implementation of Propensity Score Matching”, Journal of Economic Surveys, 22(1), 31–72.
Feng, P., Zhou, X.-H., Zou, Q.-M., Fan, M.-Y. and Li, X.-S. (2011), “Generalized Propensity Score for Estimating the Average Treatment Effect of Multiple Treatments”, Statistics in Medicine, 30: n/a. doi: 10.1002/sim.4168
Frölich, M. (2004), “Finite Sample Properties of Propensity-Score Matching and Weighting Estimators”, Review of Economics and Statistics, 86(1), 77-90.
Ghosh D. (2011), “Propensity Score Modeling in Observational Studies using Dimension Reduction Methods”, Statistics & Probability Letters, 81(7), 813-820.
Hahn, J. (1998), “On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects”, Econometrica, 66(2), 315-331.
Heckman, J., H. Ichimura, and P. Todd (1997), “Matching as an Econometric Evaluation Estimators: Evidence from Evaluating a Job Training Programme”, Review of Economic Studies, 64(4), 605- 654.
Heckman, J., R. Lalonde and J. Smith (1999). The Economics and Econometrics of Active Labor Market Programs. Handbook of Labor Economics, Volume 3, Ashenfelter, A. and D. Card, eds., Amsterdam: Elsevier Science.
Heckman, J; Ichimura, H; Smith, J; and Todd, P. (1998), “Characterizing Selection Bias using Experimental Data”, Econometrica, 66, 1017-1098.
Hirano K., G. W. Imbens and G. Ridder (2002), “Efficient Estimation of Average Treatment Effects using the Estimated Propensity Score”, Econometrica, 71(4), 1161-1190.
Ichimura, Hidehiko and Taber Christopher (2001), “Propensity-Score Matching with Instrumental Variables”, American Economic Review, 91(2): 119-124.
Imbens, G., and Wooldridge, J. (2009), “Recent Developments in the Econometrics of Program Evaluation”, Journal of Economic Literature, Vol 47(1), 5-86.
Lechner, M. (2002), “Some Practical Issues in The Evaluation of Heterogeneous Labour Market Programmes by Matching Methods”, Journal of the Royal Statistical Society. Series A, 165, 59-82.
Cuong, Nguyen Viet, 2009. "Impact evaluation of multiple overlapping programs under a conditional independence assumption," Research in Economics, Elsevier, vol. 63(1), pages 27-54.
Ravallion, M. (2001), “The Mystery of the Vanishing Benefits: An Introduction to Impact Evaluation”, The World Bank Economic Review, 15(1), 115-140.
Rosenbaum, P. and R. Rubin (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects”, Biometrika 70 (1), 41-55.
Rosenbaum, P. and R. Rubin (1985), “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score”, American Statistician, 39(1), 33-38.
Smith, J. and P. Todd. (2005), “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?”, Journal of Econometrics, 125(1–2), 305–353.
Zhao, Z. (2004), "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, 86(1), 91-107.
Zhao, Z. (2008), “Sensitivity of propensity score methods to the specifications”, Economics Letters, 98 (2008), 309–319.