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題名 A Statistical Functional Approach to Estimating Common Treatment Effects in Causal Inference
作者 陳立榜
Wu, Kuan-Hsun;Chen, Li-Pang
貢獻者 統計系
關鍵詞 IPW distribution function; network structures; propensity score; variable selection
日期 2026-03
上傳時間 20-Apr-2026 10:21:05 (UTC+8)
摘要 Estimation of treatment effects is one of crucial research problems in causal inference, and there are multiple treatment effects depending on the purpose of research targets or researchers’ interests, which include but are not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). In this study, we aim to propose the statistical functional and cumulative distribution function structure, which leads to a flexible and robust estimator and covers some frequent treatment effects. In addition, our approach also takes variable selection into account, so that informative and network structure in confounders can be identified and implemented in our estimation procedure. The theoretical properties, including variable selection consistency and asymptotic normality of the statistical functional estimator, are established. Some common treatment effects estimations are also conducted in numerical studies, and the results reveal that the proposed estimator generally outperforms the existing methods and is more efficient than its competitors.
關聯 Journal of Computational and Graphical Statistics, pp.1-24
資料類型 article
DOI https://doi.org/10.1080/10618600.2026.2648597
dc.contributor 統計系
dc.creator (作者) 陳立榜
dc.creator (作者) Wu, Kuan-Hsun;Chen, Li-Pang
dc.date (日期) 2026-03
dc.date.accessioned 20-Apr-2026 10:21:05 (UTC+8)-
dc.date.available 20-Apr-2026 10:21:05 (UTC+8)-
dc.date.issued (上傳時間) 20-Apr-2026 10:21:05 (UTC+8)-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=182136-
dc.description.abstract (摘要) Estimation of treatment effects is one of crucial research problems in causal inference, and there are multiple treatment effects depending on the purpose of research targets or researchers’ interests, which include but are not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). In this study, we aim to propose the statistical functional and cumulative distribution function structure, which leads to a flexible and robust estimator and covers some frequent treatment effects. In addition, our approach also takes variable selection into account, so that informative and network structure in confounders can be identified and implemented in our estimation procedure. The theoretical properties, including variable selection consistency and asymptotic normality of the statistical functional estimator, are established. Some common treatment effects estimations are also conducted in numerical studies, and the results reveal that the proposed estimator generally outperforms the existing methods and is more efficient than its competitors.
dc.format.extent 109 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Journal of Computational and Graphical Statistics, pp.1-24
dc.subject (關鍵詞) IPW distribution function; network structures; propensity score; variable selection
dc.title (題名) A Statistical Functional Approach to Estimating Common Treatment Effects in Causal Inference
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/10618600.2026.2648597
dc.doi.uri (DOI) https://doi.org/10.1080/10618600.2026.2648597