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The specification of the propensity score in multilevel observational studies

Listed author(s):
  • Arpino, Bruno
  • Mealli, Fabrizia

The use of multilevel models for the estimation of the propensity score for data with a hierarchical structure and unobserved cluster-level variables is proposed. This approach is compared with models that ignore the hierarchy, and models in which the hierarchy is represented by a fixed parameter for each cluster. It is shown, by simulation, that simple models with dummy variables outperform both random effect models and models ignoring the hierarchy in terms of balance of cluster-level unobserved covariates and omitted variable bias. The representation of the clusters by fixed or random effects defines a model more general than would be ideal if the relevant cluster-level variables were available. The general conclusion confirms that when conducting propensity score analysis it is safer to specify a more general model than pursuing model parsimony.

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File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00436-6
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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 55 (2011)
Issue (Month): 4 (April)
Pages: 1770-1780

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Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1770-1780
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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  1. Manski, C.F., 1989. "Nonparametric Bounds On Treatment Effects," Working papers 8909, Wisconsin Madison - Social Systems.
  2. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20035, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
  3. Leon, Andrew C. & Hedeker, Donald, 2007. "Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6114-6122, August.
  4. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
  5. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
  6. Alex Bryson, 2002. "The Union Membership Wage Premium: An Analysis Using Propensity Score Matching," CEP Discussion Papers dp0530, Centre for Economic Performance, LSE.
  7. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  8. Hong, Guanglei & Raudenbush, Stephen W., 2006. "Evaluating Kindergarten Retention Policy: A Case Study of Causal Inference for Multilevel Observational Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 901-910, September.
  9. Aassve, Arnstein & Betti, Gianni & Mazzuco, Stefano & Mencarini, Letizia, 2006. "Marital disruption and economic well-being: a comparative analysis," ISER Working Paper Series 2006-07, Institute for Social and Economic Research.
  10. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2003. "Evaluating the impact of education on earnings in the UK: Models, methods and results from the NCDS," IFS Working Papers W03/20, Institute for Fiscal Studies.
  11. Marco Caliendo & Sabine Kopeinig, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," Discussion Papers of DIW Berlin 485, DIW Berlin, German Institute for Economic Research.
  12. Dehejia, Rajeev H, 2003. "Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs with Grouped Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 1-11, January.
  13. Charles Michalopoulos & Howard S. Bloom & Carolyn J. Hill, 2004. "Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 156-179, February.
  14. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," UWO Department of Economics Working Papers 20006, University of Western Ontario, Department of Economics.
  15. 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.
  16. Oakes, J. Michael, 2004. "The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology," Social Science & Medicine, Elsevier, vol. 58(10), pages 1929-1952, May.
  17. Aassve, Arnstein & Arpino, Bruno, 2008. "Estimation of causal effects of fertility on economic wellbeing: evidence from rural Vietnam," ISER Working Paper Series 2007-27, Institute for Social and Economic Research.
  18. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  19. Friedlander, Daniel & Robins, Philip K, 1995. "Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods," American Economic Review, American Economic Association, vol. 85(4), pages 923-937, September.
  20. Wang, Qihua & Lai, Peng, 2011. "Empirical likelihood calibration estimation for the median treatment difference in observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1596-1609, April.
  21. Alex Bryson & Richard Dorsett & Susan Purdon, 2002. "The use of propensity score matching in the evaluation of active labour market policies," LSE Research Online Documents on Economics 4993, London School of Economics and Political Science, LSE Library.
  22. Zhao, Zhong, 2005. "Sensitivity of Propensity Score Methods to the Specifications," IZA Discussion Papers 1873, Institute for the Study of Labor (IZA).
  23. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
  24. Rosenbaum, Paul R. & Ross, Richard N. & Silber, Jeffrey H., 2007. "Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 75-83, March.
  25. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
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