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Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects. |
| 개인저자 | Nattino, Giovanni. |
| 단체저자명 | The Ohio State University. Biostatistics. |
| 발행사항 | [S.l.]: The Ohio State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 170 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781392380840 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
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| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Observational studies are major data sources to infer causal relationships. When using observational data to estimate causal effects, researchers must consider appropriate statistical methodology to account for the non-random allocation of the units to the treatment groups. Such methodology is well-established when the research question involves two treatment groups and results do not need to be generalized to the population from which the study sample has been selected. Relatively few studies have focused on research questions that do not fit into this framework. The goal of this work is to introduce statistical methods to perform causal inference in complex designs. First, I introduce a matching design for estimating treatment effects in the presence of multiple treatment groups. I devise a novel matching algorithm, generating samples that are well-balanced with respect to pre-treatment variables, and discuss the post-matching statistical analyses. Second, I focus on the generalization of causal effects to the population level, specifically when the sample selection is based on complex survey designs. I discuss the extension of the propensity score methodology to survey data, describe a weighted estimator for the common two-stage cluster sample and study its asymptotic properties. Third, I consider the estimation of population intervention effects, which evaluate the impact of realistic changes in the distribution of the treatment in a cohort. I describe estimators for upper and lower bounds of effects of this type, highlighting the implications for policy makers. For each of these three areas of causal inference, I use Monte Carlo simulations to assess the reliability of the proposed methods and compare them with competing approaches. The new methods are illustrated with real-data applications. Finally, I discuss limitations and aspects requiring further work. |
| 일반주제명 | Biostatistics. Public health. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
