Causal inference with non-standard experimental designs
Abstract/Contents
- Abstract
- The past decades have seen a comprehensive body of research dedicated to causal inference in conventional experimental designs. However, as technological innovations continue to foster a rapid influx of data across numerous fields, the datasets derived often exhibit new structures that stem from unconventional designs. The thesis at hand is centered around the development of methods for conducting causal inference, particularly when the design deviates from the standard, thereby making conventional methods inapplicable. Chapter 2 delves into the regression discontinuity design in cases where the running variable is a noisy measurement of a latent variable. We propose a novel design-based approach for estimation and inference. This approach proves effective when applied to a broad array of widely-used estimands. Chapter 3 explores adaptive experimentation in the context of delayed feedback. In subchapter 3.1, we extend Thompson sampling to the proportional hazard model and develop a method capable of overcoming challenges associated with vaccine trials. Subsequently, in subchapter 3.2, we study the behavior of Thompson sampling when delays are unrestricted, providing theoretical regret bounds and conducting extensive experiments. Chapter 4 investigates policy learning in scenarios involving multiple treatments or multiple outcomes. In subchapter 4.1, we propose methods for evaluating policies when cost constraints accompany multiple treatments. In subchapter 4.2, we introduce a personalized experimentation system that can learn interpretable policies from experimental data and is scalable for big datasets.
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 | Wu, Han, (Researcher in causal inference) |
|---|---|
| Degree supervisor | Wager, Stefan |
| Thesis advisor | Wager, Stefan |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Siegmund, David, 1941- |
| Degree committee member | Hastie, Trevor |
| Degree committee member | Siegmund, David, 1941- |
| 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 | Han Wu. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zn121zh7709 |
Access conditions
- Copyright
- © 2023 by Han Wu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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