Selected topics on design-based causal inference

Design-based causal inference has emerged as a robust approach for impact evaluation of interventions, programs, and policies. These methods draw causal conclusions by relying solely on the building blocks of experimental designs, thereby requiring minimal assumptions and exhibiting good statistical properties. Design-based methods are applicable to both randomized controlled trials and quasi-experimental designs. This thesis comprises three papers discussing design-based methodologies for causal inference across different study designs. The first paper proposes two enhanced randomization-based, finite-sample valid methods for inferring distributions and quantiles of individual treatment effects. The two methods demonstrate substantial power gain compared to the existing approaches in both completely randomized and stratified randomized experiments, and they can be further extended to sampling-based experiments as well as quasiexperiments constructed from matching. The second paper systematically reviews the role of randomizationbased inference in unraveling individual treatment effects in early phase vaccine trials and extends some results from the first paper to a special scenario where na¨ıve participants are not expected to exhibit responses to highly specific endpoints. We apply these methods to analyzing the immunogenicity data derived from HIV Vaccine Trials Network Study 086 and explored how different methods may facilitate decision-making and improve the evaluation of vaccine regimens. The third paper studies how to manipulate a continuous instrumental variable (IV) to facilitate causal inference in a design-based framework. We develop a non-bipartite, template matching algorithm that embeds observational data into a target, pair-randomized encouragement trial which maintains fidelity to the original study cohort while strengthening the IV. We then derive both randomization-based and biased-randomization-based inference of partial identification bounds for the sample average treatment effect in an IV-based matched pair design.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Chen, Zhe

Contributors dc:contributor

• Li, Xinran
• Zhu, Ruoqing
• Yu, Ruoqi
• Simpson, Douglas G

• Randomization Inference
• Quantile Of Individual Treatment Effect
• Instrumental Variable
• Observational Study

Statement dc:rights

• Copyright 2024 Zhe Chen

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125707

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125707