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題名 雙重機器學習在調節與中介的處方效果分析
Double/Debiased Machine Learning for Moderated and Mediated Treatment Effects Analysis
作者 楊舒羽
Yang, Shu-Yu
貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
楊舒羽
Yang, Shu-Yu
關鍵詞 雙重機器學習
處方效果
異質性
調節效果
中介變數
去除干擾因子
日期 2024
上傳時間 4-Aug-2025 14:25:02 (UTC+8)
摘要 本研究旨在利用雙重機器學習框架，探討處方效果及其異質性，並進行調節與中介效果的分析。處方效果的估計在公共政策、教育與醫療等領域中具有高度 重要性，而傳統線性迴歸方法在處理高維度資料、非線性關係與干擾因子時受到 限制，本研究採用近年廣泛應用於因果推論問題的DML方法，改善上述的挑戰，並進行兩個實證案例分析。 第一部分是以P2P借貸平台 Kiva 為例，分析借貸者照片中的正向情緒表達在 不同調節因子（如年齡等）對募資的成效；第二部分則是探討雇用頂尖服務設計師對高爾夫球場品質顧客排名的影響，並以高爾夫球場價格作為中介因子的處方 效果分析。 本研究展示DML在管理研究領域處理因果推論問題上的實用性，尤其是在需要控制多個干擾因子的情境下，DML更能準確地估計處方效果，同時在實證案 例中利用DML方法分析調節與中介的處方效果，顯示 DML 在不同領域上的應用價值。
參考文獻 Anglin, A. H., Short, J. C., Drover, W., Stevenson, R. M., McKenny, A. F., & Allison, T. H. (2018). The power of positivity? the influence of positive psychological capital language on crowdfunding performance. Journal of Business Venturing, 33(4), 470–492. Angrist, J. D. (2004). Treatment effect heterogeneity in theory and practice. The economic journal, 114(494), C52–C83. Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational studies, 5(2), 37–51. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. DoubleML. (2023). Heterogeneous treatment effects [Accessed: 2024-06-29]. https://docs.doubleml.org/stable/guide/heterogeneity.html Ellickson, P. B., Kar, W., & Reeder III, J. C. (2023). Estimating marketing component effects: Double machine learning from targeted digital promotions. Marketing Science, 42(4), 704–728. Farbmacher, H., Huber, M., Lafférs, L., Langen, H., & Spindler, M. (2022). Causal mediation analysis with double machine learning. The Econometrics Journal, 25(2), 277–300. Lee, S. J., Heim, G. R., & Ketzenberg, M. (2013). Impacts of top service designers: Analysis of golf course quality and price [Working Paper]. Luthans, F., Luthans, K. W., & Luthans, B. C. (2004). Positive psychological capital: Beyond human and social capital. Oreopoulos, P. (2006). Estimating average and local average treatment effects of education when compulsory schooling laws really matter. American Economic Review, 96(1), 152–175. Park, T., Paudel, K., & Sene, S. (2018). Sales impacts of direct marketing choices: Treatment effects with multinomial selectivity. European Review of Agricultural Economics, 45(3), 433–453. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(469), 322–331. Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with sas and spss macros. Psychological methods, 18(2), 137. Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. M. (2023). Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine. BMC Medical Informatics and Decision Making, 23(1), 110. Yang, J.-C., Chuang, H.-C., & Kuan, C.-M. (2020). Double machine learning with gradient boosting and its application to the big in audit quality effect. Journal of Econometrics, 216(1), 268–283. Yin, W., Huo, W., & Lin, D. (2023). The effects of state coercion on voting outcome in protest movements: A causal forest approach. Political Science Research and Methods, 11(3), 645–653. 黃紀. (2008). 因果推論與觀察研究: [反事實模型] 之思考. 社會科學論叢, 2(1), 1–22.
描述 碩士
國立政治大學
資訊管理學系
111356036
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111356036
資料類型 thesis
dc.contributor.advisor 莊皓鈞<br>周彥君zh_TW
dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chunen_US
dc.contributor.author (Authors) 楊舒羽zh_TW
dc.contributor.author (Authors) Yang, Shu-Yuen_US
dc.creator (作者) 楊舒羽zh_TW
dc.creator (作者) Yang, Shu-Yuen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-Aug-2025 14:25:02 (UTC+8)-
dc.date.available 4-Aug-2025 14:25:02 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:25:02 (UTC+8)-
dc.identifier (Other Identifiers) G0111356036en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158565-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 111356036zh_TW
dc.description.abstract (摘要) 本研究旨在利用雙重機器學習框架，探討處方效果及其異質性，並進行調節與中介效果的分析。處方效果的估計在公共政策、教育與醫療等領域中具有高度 重要性，而傳統線性迴歸方法在處理高維度資料、非線性關係與干擾因子時受到 限制，本研究採用近年廣泛應用於因果推論問題的DML方法，改善上述的挑戰，並進行兩個實證案例分析。 第一部分是以P2P借貸平台 Kiva 為例，分析借貸者照片中的正向情緒表達在 不同調節因子（如年齡等）對募資的成效；第二部分則是探討雇用頂尖服務設計師對高爾夫球場品質顧客排名的影響，並以高爾夫球場價格作為中介因子的處方 效果分析。 本研究展示DML在管理研究領域處理因果推論問題上的實用性，尤其是在需要控制多個干擾因子的情境下，DML更能準確地估計處方效果，同時在實證案 例中利用DML方法分析調節與中介的處方效果，顯示 DML 在不同領域上的應用價值。zh_TW
dc.description.tableofcontents 第一章 緒論 1 　　第一節 研究背景與動機 .............................. 1 第二章 處方效果與雙重機器學習 5 　　第一節 處方效果 ................................... 5 　　第二節 雙重機器學習 ............................... 6 第三章 受干擾的處方效果分析 9 　　第一節 資料與變數 ................................. 9 　　第二節 估計結果 .................................. 13 第四章 受中介因子的處方效果分析 21 　　第一節 因果中介分析 .............................. 21 　　第二節 資料與變數 ................................ 24 　　第三節 估計結果 .................................. 26 第五章 結論 29 Reference .......................................... 30zh_TW
dc.format.extent 2067333 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111356036en_US
dc.subject (關鍵詞) 雙重機器學習zh_TW
dc.subject (關鍵詞) 處方效果zh_TW
dc.subject (關鍵詞) 異質性zh_TW
dc.subject (關鍵詞) 調節效果zh_TW
dc.subject (關鍵詞) 中介變數zh_TW
dc.subject (關鍵詞) 去除干擾因子zh_TW
dc.title (題名) 雙重機器學習在調節與中介的處方效果分析zh_TW
dc.title (題名) Double/Debiased Machine Learning for Moderated and Mediated Treatment Effects Analysisen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Anglin, A. H., Short, J. C., Drover, W., Stevenson, R. M., McKenny, A. F., & Allison, T. H. (2018). The power of positivity? the influence of positive psychological capital language on crowdfunding performance. Journal of Business Venturing, 33(4), 470–492. Angrist, J. D. (2004). Treatment effect heterogeneity in theory and practice. The economic journal, 114(494), C52–C83. Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational studies, 5(2), 37–51. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. DoubleML. (2023). Heterogeneous treatment effects [Accessed: 2024-06-29]. https://docs.doubleml.org/stable/guide/heterogeneity.html Ellickson, P. B., Kar, W., & Reeder III, J. C. (2023). Estimating marketing component effects: Double machine learning from targeted digital promotions. Marketing Science, 42(4), 704–728. Farbmacher, H., Huber, M., Lafférs, L., Langen, H., & Spindler, M. (2022). Causal mediation analysis with double machine learning. The Econometrics Journal, 25(2), 277–300. Lee, S. J., Heim, G. R., & Ketzenberg, M. (2013). Impacts of top service designers: Analysis of golf course quality and price [Working Paper]. Luthans, F., Luthans, K. W., & Luthans, B. C. (2004). Positive psychological capital: Beyond human and social capital. Oreopoulos, P. (2006). Estimating average and local average treatment effects of education when compulsory schooling laws really matter. American Economic Review, 96(1), 152–175. Park, T., Paudel, K., & Sene, S. (2018). Sales impacts of direct marketing choices: Treatment effects with multinomial selectivity. European Review of Agricultural Economics, 45(3), 433–453. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(469), 322–331. Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with sas and spss macros. Psychological methods, 18(2), 137. Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. M. (2023). Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine. BMC Medical Informatics and Decision Making, 23(1), 110. Yang, J.-C., Chuang, H.-C., & Kuan, C.-M. (2020). Double machine learning with gradient boosting and its application to the big in audit quality effect. Journal of Econometrics, 216(1), 268–283. Yin, W., Huo, W., & Lin, D. (2023). The effects of state coercion on voting outcome in protest movements: A causal forest approach. Political Science Research and Methods, 11(3), 645–653. 黃紀. (2008). 因果推論與觀察研究: [反事實模型] 之思考. 社會科學論叢, 2(1), 1–22.zh_TW