Causal inference : methodological advances with an application to climate impact

Placeholder Show Content


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.


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


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


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.

Access conditions

© 2021 by Xinkun Nie

Also listed in

Loading usage metrics...