Nonparametric Regression Methods for Causal Mediation Analysis
View/ Open
chapter2.tex (45.24Kb)
chapter1.tex (39.19Kb)
dissertation.tex (1.650Kb)
Dissertate.cls (8.707Kb)
Access Status
Full text of the requested work is not available in DASH at this time ("restricted access"). For more information on restricted deposits, see our FAQ.Author
Devick, Katrina Leigh
Metadata
Show full item recordCitation
Devick, Katrina Leigh. 2018. Nonparametric Regression Methods for Causal Mediation Analysis. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.Abstract
Causal mediation analysis is a popular approach to quantify the mechanisms through which an exposure operates on an outcome. Using causal mediation analysis, one can decompose the total effect (TE) of an exposure on an outcome into the pathway that operates indirectly through an intermediate (mediator) variable and the pathway that is independent of the mediator variable or that operates directly from the exposure to outcome. Researchers' understanding of the pathways operating through an intermediate variable is crucial for policy recommendations to reduce the potentially harmful impact of the exposure(s) and/or uneven burden of disease. With the growing interest in causal mediation analysis, new methods are needed to estimate direct and indirect effects with complex data. In this dissertation, I propose three novel approaches to estimate mediation effects using Bayesian nonparametric regression models. These methods allow for data with the following complexities: (Chapter 1) the exposure is binary and nonmanipulable and a normality assumption for the mediator variable is not suitable; (Chapter 2) the joint effect of multiple exposures (mixture) is of interest; and (Chapter 3) the exposure of interest is a mixture and the mediator and outcome variables are latent constructs composed of multiple measurements. For each method, I discuss how my approach addresses gaps in the literature and demonstrate how my proposed approach preforms compared to current methods via simulation and data application.Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:41128501
Collections
- FAS Theses and Dissertations [5858]
Contact administrator regarding this item (to report mistakes or request changes)