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Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation

Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis.

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    Date Created
    • 2013
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  • Text
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    • Partial requirement for: Ph. D., Arizona State University, 2013
      Note type
      thesis
    • Includes bibliographical references (p. 77-81)
      Note type
      bibliography
    • Field of study: Psychology

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    by Hei Ning Cham

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