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Propensity score methods for merging observational and experimental datasets
Rosenman, EvanStanford, Calif. : Department of Statistics, Stanford University, May 2018.Online statistics.stanford.edu
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Making causal conclusions from heterogeneous data sources
Rosenman, Evan Taylor Ragosa[Stanford, California] : [Stanford University], 2020The modern proliferation of large observational databases -- in fields such as e-commerce and electronic health -- presents challenges and opportunities for applied researchers. Such data can contain rich information about causal effects of interest, but the effects can only be estimated if we make untestable assumptions and carefully model the assignment mechanism. Experimental data provides a "virtuous" counterpart for the purposes of inferring causal effects, but randomized trials are often limited in size and, consequentially, lack precision. In this thesis, we consider problems of "data fusion, " in which observational and experimental datasets are used together to estimate causal effects. The problem is considered from three angles. First, we develop methods for merging experimental and observational causal effect estimates in the case when all confounding variables are measured in the observational studies. Next, we remove the unconfoundedness assumption, which leads to a new class of estimators based on a shrinkage approach. Finally, we propose a novel solution for designing experiments informed by observational studies, making use of the regret minimization framework. Throughout, we deploy tools from disparate areas of the literature, including Empirical Bayes, decision theory, and convex optimization
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