CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

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dc.contributor.advisorSchafer, Joseph L.en_US
dc.contributor.advisorSmith, Paul J.en_US
dc.contributor.authorGalagate, Douglasen_US
dc.contributor.departmentMathematical Statisticsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2016-06-22T05:40:35Z
dc.date.available2016-06-22T05:40:35Z
dc.date.issued2016en_US
dc.description.abstractCausal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.en_US
dc.identifierhttps://doi.org/10.13016/M2Q48K
dc.identifier.urihttp://hdl.handle.net/1903/18170
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledBiostatisticsen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pquncontrolledaverage causal effecten_US
dc.subject.pquncontrolledcausal inferenceen_US
dc.subject.pquncontrolleddose-responseen_US
dc.subject.pquncontrolledpotential outcomesen_US
dc.subject.pquncontrolledpropensity scoreen_US
dc.subject.pquncontrolledweightingen_US
dc.titleCAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.en_US
dc.typeDissertationen_US

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