Robust causal inference and machine learning with clinical applications
Abstract/Contents
- Abstract
- As healthcare data becomes increasingly ubiquitous, improving data-driven biomedical research is timely and important. There is a rush to learn from these new sources of data, and to implement research findings into clinical practice. While machine learning methods provide compelling examples of recognizing sophisticated patterns in data, their impact rests heavily on their ability to use data to influence decision making, especially in healthcare. The relationship between machine learning and decision making becomes particularly clear through the lens of causal inference. In general, the harm and benefit attributed to a medical decision depends on the causal treatment effect of the decision in the appropriate population, beyond their baseline risk of poor outcomes. In precision medicine research, the goal is to develop treatment decisions for individual patients by considering the sub-population of individuals with similar covariates to each patient. This thesis advances methodology and practice for applying machine learning to learn better decision-making rules that influence clinical practice, and understanding the fundamental possibilities and limitations of using data to learn to make optimal decisions. First, we develop an approach for personalized treatment effect estimation based on the relative ratio of treatment outcomes. Second, we study when we can trust causal results learned from data, and develop a sensitivity analysis for conditional and average treatment effects to bound the bias created from unobserved confounding. Third, noting that treatment benefit is highly correlated with baseline risk for preventative treatments for atherosclerotic cardiovascular disease (ASCVD), we use machine learning approaches to improve ASCVD risk predictions from longitudinal cohort data that affect clinical prescribing practice, particularly among under-represented minorities
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yadlowsky, Steven |
|---|---|
| Degree supervisor | Tian, Lu |
| Thesis advisor | Tian, Lu |
| Thesis advisor | Duchi, John |
| Thesis advisor | Van Roy, Benjamin |
| Degree committee member | Duchi, John |
| Degree committee member | Van Roy, Benjamin |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Steven Michael Yadlowsky |
|---|---|
| Note | Submitted to the Department of Electrical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Steven Yadlowsky
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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