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Causal Inference with observational data in CSNI(cluster-specific non-ignorable) condition : CSNI 조건에서의 관측 자료를 이용한 인과관계 분석

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Authors
김지수
Advisor
Myunghee Cho Paik
Major
자연과학대학 통계학과
Issue Date
2015-02
Publisher
서울대학교 대학원
Keywords
causal inference
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2015. 2. Myunghee Cho Paik.
Abstract
Due to absence of randomization procedure, causal inference is a lot more delicate procedure with observational data as compared with experimental data. The propensity score weighting method is being increasingly used in such disciplines so as to achieve balance of observed confounders in order to remove their confounding effects. However, this method hasnt been developed so far so as to cope with unmeasured confounders efficiently. Meanwhile, observational data often have analytically relevant multilevel or clustered structure, which can be the source of cluster-specific unmeasured confounders. In this paper, we propose an unbiased estimator for the population average causal effect of a binary treatment from observational data when the treatment assignment mechanism is assumed to be CSNI (cluster-specific non-ignorable). We also propose a consistent estimator of its variance, using standard linearization method. This new estimator turns out to satisfy consistency and asymptotic normality also, which will be proven successively.
Language
English
URI
https://hdl.handle.net/10371/131289
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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