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Causal inference from nonrandomized data: key concepts and recent trends

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dc.contributor.author최영근-
dc.contributor.author유동현-
dc.date.available2021-02-22T05:28:50Z-
dc.date.issued2019-04-
dc.identifier.issn1225-066X-
dc.identifier.issn2383-5818-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1859-
dc.description.abstractCausal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman- Rubin’s potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl’s structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin’s potential outcome model.-
dc.format.extent13-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국통계학회-
dc.titleCausal inference from nonrandomized data: key concepts and recent trends-
dc.title.alternative비실험 자료로부터의 인과 추론: 핵심 개념과 최근 동향-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.5351/KJAS.2019.32.2.173-
dc.identifier.wosid000497646200001-
dc.identifier.bibliographicCitation응용통계연구, v.32, no.2, pp 173 - 185-
dc.citation.title응용통계연구-
dc.citation.volume32-
dc.citation.number2-
dc.citation.startPage173-
dc.citation.endPage185-
dc.identifier.kciidART002463803-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassesci-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.identifier.urlhttp://kiss.kstudy.com/thesis/thesis-view.asp?key=3677405-
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