政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148694
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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/148694


    Title: CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
    Authors: 陳立榜
    Chen, Li-Pang;Hsu, Wei-Hsin
    Contributors: 統計系
    Keywords: 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.
    Date: 2023-09
    Issue Date: 2023-12-13 13:55:00 (UTC+8)
    Relation: Japanese Journal of Statistics and Data Science (Invited submission for the special issue: Recent Advances in Biostatistics)
    Data Type: article
    DOI link: https://doi.org/10.1007/s42081-023-00217-y
    DOI: 10.1007/s42081-023-00217-y
    Appears in Collections:[Department of Statistics] Periodical Articles

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