Making causal conclusions from heterogeneous data sources
Abstract/Contents
- Abstract
- The 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
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Rosenman, Evan Taylor Ragosa |
|---|---|
| Degree supervisor | Baiocchi, Michael |
| Degree supervisor | Owen, Art B |
| Thesis advisor | Baiocchi, Michael |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Palacios Roman, Julia Adela |
| Degree committee member | Palacios Roman, Julia Adela |
| Associated with | Stanford University, Department of Statistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Evan Taylor Ragosa Rosenman |
|---|---|
| Note | Submitted to the Department of Statistics |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Evan Taylor Ragosa Rosenman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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