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The specification of the propensity score in multilevel observational studies

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Author Info
Arpino, Bruno
Mealli, Fabrizia

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Abstract

Propensity Score Matching (PSM) has become a popular approach to estimation of causal effects. It relies on the assumption that selection into a treatment can be explained purely in terms of observable characteristics (the “unconfoundedness assumption”) and on the property that balancing on the propensity score is equivalent to balancing on the observed covariates. Several applications in social sciences are characterized by a hierarchical structure of data: units at the first level (e.g., individuals) clustered into groups (e.g., provinces). In this paper we explore the use of multilevel models for the estimation of the propensity score for such hierarchical data when one or more relevant cluster-level variables is unobserved. We compare this approach with alternative ones, like a single level model with cluster dummies. By using Monte Carlo evidence we show that multilevel specifications usually achieve reasonably good balancing in cluster level unobserved covariates and consequently reduce the omitted variable bias. This is also the case for the dummy model.

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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 17407.

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Date of creation: 2008
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Handle: RePEc:pra:mprapa:17407

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Related research
Keywords: propensity score; multilevel studies; unconfoundedness; causal inference;

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Find related papers by JEL classification:
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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  1. 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. [Downloadable!]
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  2. Zhong Zhao, 2005. "Sensitivity of Propensity Score Methods to the Specifications," IZA Discussion Papers 1873, Institute for the Study of Labor (IZA). [Downloadable!]
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  3. Alex Bryson, 2002. "The Union Membership Wage Premium: An Analysis Using Propensity Score Matching," CEP Discussion Papers dp0530, Centre for Economic Performance, LSE. [Downloadable!]
  4. 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. [Downloadable!]
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  5. 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)
  6. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Blackwell Publishing, vol. 64(4), pages 605-54, October. [Downloadable!] (restricted)
  7. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May. [Downloadable!] (restricted)
  8. Arnstein Aassve & Gianni Betti & Stefano Mazzuco & Letizia Mencarini, 2007. "Marital disruption and economic well-being: a comparative analysis," Journal Of The Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 781-799. [Downloadable!] (restricted)
  9. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Blackwell Publishing, vol. 22(1), pages 31-72, 02. [Downloadable!] (restricted)
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  10. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, 06. [Downloadable!] (restricted)
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  11. 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 923-37, September. [Downloadable!] (restricted)
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This page was last updated on 2009-12-21.

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