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The specification of the propensity score in multilevel observational studies

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  • Arpino, Bruno
  • Mealli, Fabrizia

Abstract

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.

Suggested Citation

  • Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1770-1780
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    Cited by:

    1. Miquel-Àngel Garcia-López & Albert Solé-Ollé & Elisabet Viladecans-Marsal, 2014. "Do land use policies follow road construction?," Working Papers 2014/2, Institut d'Economia de Barcelona (IEB).
    2. repec:eee:csdana:v:113:y:2017:i:c:p:88-99 is not listed on IDEAS

    More about this item

    Keywords

    Causal inference Multilevel studies Propensity score Unconfoundedness;

    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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