Performance of Propensity Score Methods in the Presence of Heterogeneous Treatment Effects

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dc.contributor.advisorStapleton, Laura Men_US
dc.contributor.authorStepien, Kathleen Mariaen_US
dc.contributor.departmentMeasurement, Statistics and Evaluationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-01-24T06:45:25Z
dc.date.available2017-01-24T06:45:25Z
dc.date.issued2016en_US
dc.description.abstractEstimating an average treatment effect assumes that individuals and groups are homogeneous in their responses to a treatment or intervention. However, treatment effects are often heterogeneous. Selecting the most effective treatment, generalizing causal effect estimates to a population, and identifying subgroups for which a treatment is effective or harmful are factors that motivate the study of heterogeneous treatment effects. In observational studies, treatment effects are often estimated using propensity score methods. This dissertation adds to the literature on the analysis of heterogeneous treatment effects using propensity score methods. Three propensity score methods were compared using Monte Carlo simulation: single propensity score with exact matching on subgroup, matching using group propensity scores, and multinomial propensity scores using generalized boosted modeling. Methods were evaluated under various group distributions, sample sizes, effect sizes, and selection models. An empirical analysis using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) is included to demonstrate the methods studied. Simulation results showed that estimating group propensity scores provided the smallest MSE, MNPS performance was comparable to GBM, and including the group indicator in the propensity score model improved treatment effect estimates regardless of whether group membership influenced selection. In addition, subclassification performed poorly when one group was more prevalent in the extremes of the propensity score distribution.en_US
dc.identifierhttps://doi.org/10.13016/M2483S
dc.identifier.urihttp://hdl.handle.net/1903/18997
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledBiostatisticsen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pquncontrolledCausal Effectsen_US
dc.subject.pquncontrolledHeterogeneous Treatment Effectsen_US
dc.subject.pquncontrolledPropensity Scoreen_US
dc.titlePerformance of Propensity Score Methods in the Presence of Heterogeneous Treatment Effectsen_US
dc.typeDissertationen_US

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