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Causal Inference Using Propensity Score Matching in Clustered Data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Propensity score matching is commonly used to estimate causal effects of treatments. However, when using data with a hierarchical structure, we need to take the multilevel nature of the data into account. In this thesis the estimation of propensity scores with multilevel models is presented to extend propensity score matching for use with multilevel data. A Monte Carlo simulation study is performed to evaluate several different estimators. It is shown that propensity score estimators ignoring the multilevel structure of the data are biased, while fixed effects models produce unbiased results. An empirical study of the causal effect of truancy on mathematical ability for Swedish 9th graders is also performed, where it is shown that truancy has a negative effect on mathematical ability.

Place, publisher, year, edition, pages
2014. , 33 p.
Keyword [en]
potential outcomes, multilevel modeling, random effects, hierarchical data
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-225990OAI: diva2:723279
External cooperation
Educational program
Master Programme in Statistics
2014-06-02, Uppsala, 14:20 (English)
Available from: 2014-06-24 Created: 2014-06-10 Last updated: 2014-06-24Bibliographically approved

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