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Causal Inference under Data Restrictions

Tan, Xiaoqing (2022) Causal Inference under Data Restrictions. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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This dissertation focuses on modern causal inference under uncertainty and data restrictions, with applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making.

In the first project, we propose a method under the principal stratification framework to identify and estimate the average treatment effects on a binary outcome, conditional on the counterfactual status of a post-treatment intermediate response. Under mild assumptions, the treatment effect of interest can be identified. We extend the approach to address censored outcome data. The proposed method is applied to a neoadjuvant clinical trial and its performance is evaluated via simulation studies.

In the second project, we propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data. To our best knowledge, there is no established model averaging approach for distributed data with a focus on improving the estimation of treatment effects. The performance of this approach is demonstrated by a study of the causal effects of oxygen therapy on hospital survival rate and backed up by comprehensive simulations.

In the third project, we propose a robust individualized decision learning framework with sensitive variables to improve the worst-case outcomes of individuals caused by sensitive variables that are unavailable at the time of decision. Unlike most existing work that uses mean-optimal objectives, we propose a robust learning framework via finding a newly defined quantile- or infimum-optimal decision rule. From a causal perspective, we also generalize the classic notion of (average) fairness to conditional fairness for individual subjects. The reliable performance of the proposed method is demonstrated through synthetic experiments and three real-data applications.

Public health significance: The dissertation addresses several aspects of causal inference: 1) identify principal stratum treatment effects; 2) enhance the estimation of treatment effects via heterogeneous data integration; 3) derive robust individualized decision rules considering worst-case scenarios. It has the potential to fundamentally improve the current practice in drug development and precision medicine.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Tan, Xiaoqingxit31@pitt.eduxit310000-0002-6342-5634
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairTang, Lulutang@pitt.edulutang
Committee CoChairTang, Gonggot1@pitt.edugot1
Committee MemberChang, Chung-Chou Hochangj@pitt.educhangj
Committee MemberBrant, Emilybranteb@pitt.edubranteb
Committee MemberQi,
Date: 30 August 2022
Date Type: Publication
Defense Date: 25 July 2022
Approval Date: 30 August 2022
Submission Date: 5 August 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 148
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Machine learning, Conditional average treatment effects, Principal stratum, Model averaging, Robust decision making, Fairness
Date Deposited: 30 Aug 2022 14:04
Last Modified: 30 Aug 2022 14:04


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