The specification of the propensity score in multilevel observational studies

Bruno Arpino and Fabrizia Mealli (mealli@ds.unifi.it)

Computational Statistics & Data Analysis, 2011, vol. 55, issue 4, 1770-1780

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.

Keywords: Causal; inference; Multilevel; studies; Propensity; score; Unconfoundedness (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00436-6
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: The specification of the propensity score in multilevel observational studies (2008)
Working Paper: The specification of the propensity score in multilevel observational studies (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:4:p:1770-1780

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).