Causal inference : methodological advances with an application to climate impact
Abstract/Contents
- Abstract
- Causal inference lies at the heart of leveraging data to evaluate interventions and to inform policy decisions. This thesis makes three methodology contributions. The first two introduce methods in leveraging flexible machine learning models for causal estimators, in the context of estimating heterogeneous treatment effects and learning dynamic treatment policies respectively. The third contribution provides a framework to generalize results from randomized trials across locations. I will conclude with an application of applying the state-of-the-art causal methodology to studying the impact of wildfire risk perception on the California residential real estate market.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Nie, Xinkun |
|---|---|
| Degree supervisor | Brunskill, Emma |
| Degree supervisor | Wager, Stefan |
| Thesis advisor | Brunskill, Emma |
| Thesis advisor | Wager, Stefan |
| Thesis advisor | Liang, Percy |
| Degree committee member | Liang, Percy |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xinkun Nie. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/xx110yj0513 |
Access conditions
- Copyright
- © 2021 by Xinkun Nie
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