Three essays on causal inference
Abstract/Contents
- Abstract
- This thesis describes three research projects in causal inference, all related to the problem of contrasting the average counterfactual outcomes on two sides of a binary decision. In the first project, we discuss estimation of the average causal effect in a randomized control trial. Here, we find that statisticians find themselves in a kind of statistical paradise: a simple model-based procedure delivers correct confidence intervals even if the experimental participants are not randomly sampled and mis-specified models are used. In the second project, we consider the problem of testing for a treatment effect using observational data with no hidden confounders. Conceptually, this is no different from a rather complicated RCT, and one might expect that a return to statistical paradise is possible. Unfortunately, this is not the case: we show that even intuitively reasonable uses of correct models may still yield misleading conclusions. The final project looks at observational data with unobserved confounding and gives methods for computing bounds on average causal effects. Here, we discover some never-before-seen robustness properties unique to the partially-identified setting.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Guo, Kevin Xinkai |
|---|---|
| Degree supervisor | Rothenhäusler, Dominik, 1989- |
| Thesis advisor | Rothenhäusler, Dominik, 1989- |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Romano, Joseph P, 1960- |
| Degree committee member | Owen, Art B |
| Degree committee member | Romano, Joseph P, 1960- |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Statistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Kevin Guo. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/nd430jy2483 |
Access conditions
- Copyright
- © 2023 by Kevin Xinkai Guo
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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