Essays in political methodology
Abstract/Contents
- Abstract
- This dissertation is comprised of four chapters, which are united by their focus on the applications of machine learning methods to improve the robustness and flexibility of causal inference strategies commonly used by social scientists. Chapter 1 proposes methods that use spatial smoothing techniques to flexibly adjust for unobserved spatial confounders for observational causal inference problems. Chapter 2 examines the robustness of instrumental variables strategies in a decade's worth of published articles in political science and provides guidelines for empirical practice related to inference with weak instruments. Chapter 3 introduces a framework for estimating treatment effects by combining predictions from machine learning models with weights that balance observable characteristics across treatment and control groups in a variety of data settings frequently encountered by social scientists. Chapter 4 applies these methods to the question of whether private election funding affected turnout and votes for the democratic party in the 2020 election, and finds scant evidence in favor of popular narratives that these grants swung the election.
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 | Lal, Apoorva |
|---|---|
| Degree supervisor | Acharya, Avidit |
| Degree supervisor | Hainmueller, Jens |
| Thesis advisor | Acharya, Avidit |
| Thesis advisor | Hainmueller, Jens |
| Thesis advisor | Grimmer, Justin |
| Thesis advisor | Wager, Stefan |
| Degree committee member | Grimmer, Justin |
| Degree committee member | Wager, Stefan |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Political Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Apoorva Lal. |
|---|---|
| Note | Submitted to the Department of Political Science. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/kp880nx7202 |
Access conditions
- Copyright
- © 2023 by Apoorva Lal
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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