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    題名: CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
    作者: 陳立榜
    Chen, Li-Pang;Hsu, Wei-Hsin
    貢獻者: 統計系
    關鍵詞: In this paper, we study causal inference with complex and noisy data accommodated. A new structure is called CHEMIST, which refers to Causal inference with High-dimensional Error-prone covariates and MISclassified Treatments. To suitably tackle those challenges when estimating the average treatment effect (ATE), we develop the FATE method, which reflects Feature screening, Adaptive lasso, Treatment adjustment, and Error elimination in covariates, to handle variable selection and measurement error correction. Under informative and error-eliminated data, we can estimate the ATE. To make our strategy available for public use, we develop a new R package CHEMIST, which provides functions for users to estimate the ATE. With the flexibility of arguments, one can examine different scenarios based on our package. In this paper, we introduce the FATE method and the implementation in the R package CHEMIST. Moreover, we demonstrate applications in two real data sets.
    日期: 2023-09
    上傳時間: 2023-12-13 13:55:00 (UTC+8)
    關聯: Japanese Journal of Statistics and Data Science (Invited submission for the special issue: Recent Advances in Biostatistics)
    資料類型: article
    DOI 連結: https://doi.org/10.1007/s42081-023-00217-y
    DOI: 10.1007/s42081-023-00217-y
    顯示於類別:[統計學系] 期刊論文

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