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題名 統計機器學習：理論建構與因果分析
Statistical Machine Learning for Theory-building and Causal Analysis作者 周平
Chou, Ping貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
周平
Chou, Ping關鍵詞 理論建構
統計機器學習
雙重機器學習
統計與因果推論
人機互動
Theory building
statistical machine learning
double machine learning
statistical and causal inference
human-system interaction日期 2023 上傳時間 1-Feb-2024 10:56:09 (UTC+8) 摘要 解決管理問題並提供決策支援是管理研究的核心議題，其中，機器學習模型，尤其具有黑箱性質、被廣泛用於數學函數估計的演算法，在決策最佳化過程所需的預測及處方性分析扮演了關鍵的角色。儘管如此，在實證研究中，以機器學習方法幫助驗證理論有效或為真的效用並未吸引多數管理研究者的關注。隨著統計機器學習及通用性的模型解釋技術的進展，機器學習已成為統計及因果推論的有力工具。在探索變數相關性的描述性理論建構，少數頂尖管理期刊文章近來提倡將機器學習用於模式的探索和理論建構。然而，這些研究並未對變數及交互作用的重要性進行統計檢定，對於現有的統計機器學習技術亦缺乏系統性的整合。而在分析變數因果關係的規範性理論建構，計量經濟文獻雖然發展了基於機器學習的估計方法，但文獻仍缺乏相關技術在資訊及作業管理的實證應用。為彌補上述研究缺口，本學位論文包含了兩篇研究。第一篇研究闡述了統計機器學習如何基於相關性進行解釋及驗證性的理論建構。當中，本研究提出了一套以隨機森林為基礎的研究流程，能提供演繹及溯因推理性的理論建構，針對變數與交互作用的重要性進行統計檢定，從而偵測資料中隱藏的模式並測試變數的相關性。透過廣泛的模擬實驗，本研究指出機器學習有較卓越表現的情境，以及與傳統統計分析方法互補的情境。第二篇研究介紹了一套基於機器學習的通用因果推論技術──雙重機器學習；本研究除透過模擬實驗來驗證該技術在因果推論的有效性及穩健性，同時將其應用於零售供應鏈情境下的庫存管理實證分析，探討半自動化的決策支援系統建議與經理人修正的人機互動模式。本研究從動態面板數據中發掘了數個穩健的因果模式，這些模式指出庫存管理績效可能因為經理人修改系統決策而得到改善的情境。綜上所述，本學位論文的兩篇研究皆致力於整合、驗證，及應用前沿的機器學習於理論建構，從而為方法論文獻（機器學習的統計推論）及實證作業管理和人機互動研究提出貢獻。
Problem-solving and decision support represent a prominent discipline in the management literature, whereas Machine Learning (ML) – the black-box algorithms dedicated to function-fitting in particular – serves as pivotal tools for predictive and prescriptive analytics for decision optimization. However, management researchers tend to overlook ML’s potential as a research methodology for empirical research, which answers the central inquiry of theory building “Is it valid or true.” The up-to-date developments in statistical ML and model-agnostic interpretation have rendered the algorithms decent statistical and causal inference tools. For descriptive theory development reliant on association, recent works from authoritative management journals have begun using ML for pattern discovery and theory building. Nevertheless, they fall short of offering statistical tests for variable/interaction importance and integrating them with the extant techniques. For causality that facilitates normative theory, the econometrics literature has devised ML-based causal effect estimators but lacks empirical application in information and operations management research. To fill in the gaps, this dissertation comprises two essays. The first essay articulates the utilization of statistical ML for explanatory and confirmatory theory-building with associations. We propose an analysis protocol based on Random Forest (RF) for abductive and deductive theory building, equipped with statistical tests for the significance of variables and the interactions. The protocol helps uncover the patterns and test the association. Based on extensive simulations, we shed light on the context where MLs exhibit superiority and are complementary to the prevalently used econometrics model for empirical data analysis. For the second essay, we introduce Double Machine Learning (DML), a general framework of ML-based causal inference, for theory building. In addition to numerically assessing the statistical properties/robustness of DML, we use DML to empirically analyze human-system interaction in the context of semi-automated replenishment decision-making. Reliant on the proven statistical validity of DML for dynamic panel data, we uncover robust causal patterns for the contexts where managers’ interventions to the algorithmic decisions improve inventory performance. The two essays are dedicated to integrating, validating, and employing sophisticated ML techniques for theory building, and make contributions to the methodological literature on ML-based inference in management and the literature on empirical operations management and human-system interaction in decision-making.參考文獻 Agrawal, A., Gans, J., & Goldfarb, A. (2020). How to win with machine learning. Harvard Business Review. Allcott, H., Braghieri, L., and Gentzkow, M. (2020). The welfare effects of social media. American Economic Review, 110(3), 629-676. 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國立政治大學
資訊管理學系
108356508資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356508 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-Chun en_US dc.contributor.author (Authors) 周平 zh_TW dc.contributor.author (Authors) Chou, Ping en_US dc.creator (作者) 周平 zh_TW dc.creator (作者) Chou, Ping en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Feb-2024 10:56:09 (UTC+8) - dc.date.available 1-Feb-2024 10:56:09 (UTC+8) - dc.date.issued (上傳時間) 1-Feb-2024 10:56:09 (UTC+8) - dc.identifier (Other Identifiers) G0108356508 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/149466 - dc.description (描述) 博士 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) 108356508 zh_TW dc.description.abstract (摘要) 解決管理問題並提供決策支援是管理研究的核心議題，其中，機器學習模型，尤其具有黑箱性質、被廣泛用於數學函數估計的演算法，在決策最佳化過程所需的預測及處方性分析扮演了關鍵的角色。儘管如此，在實證研究中，以機器學習方法幫助驗證理論有效或為真的效用並未吸引多數管理研究者的關注。隨著統計機器學習及通用性的模型解釋技術的進展，機器學習已成為統計及因果推論的有力工具。在探索變數相關性的描述性理論建構，少數頂尖管理期刊文章近來提倡將機器學習用於模式的探索和理論建構。然而，這些研究並未對變數及交互作用的重要性進行統計檢定，對於現有的統計機器學習技術亦缺乏系統性的整合。而在分析變數因果關係的規範性理論建構，計量經濟文獻雖然發展了基於機器學習的估計方法，但文獻仍缺乏相關技術在資訊及作業管理的實證應用。為彌補上述研究缺口，本學位論文包含了兩篇研究。第一篇研究闡述了統計機器學習如何基於相關性進行解釋及驗證性的理論建構。當中，本研究提出了一套以隨機森林為基礎的研究流程，能提供演繹及溯因推理性的理論建構，針對變數與交互作用的重要性進行統計檢定，從而偵測資料中隱藏的模式並測試變數的相關性。透過廣泛的模擬實驗，本研究指出機器學習有較卓越表現的情境，以及與傳統統計分析方法互補的情境。第二篇研究介紹了一套基於機器學習的通用因果推論技術──雙重機器學習；本研究除透過模擬實驗來驗證該技術在因果推論的有效性及穩健性，同時將其應用於零售供應鏈情境下的庫存管理實證分析，探討半自動化的決策支援系統建議與經理人修正的人機互動模式。本研究從動態面板數據中發掘了數個穩健的因果模式，這些模式指出庫存管理績效可能因為經理人修改系統決策而得到改善的情境。綜上所述，本學位論文的兩篇研究皆致力於整合、驗證，及應用前沿的機器學習於理論建構，從而為方法論文獻（機器學習的統計推論）及實證作業管理和人機互動研究提出貢獻。 zh_TW dc.description.abstract (摘要) Problem-solving and decision support represent a prominent discipline in the management literature, whereas Machine Learning (ML) – the black-box algorithms dedicated to function-fitting in particular – serves as pivotal tools for predictive and prescriptive analytics for decision optimization. However, management researchers tend to overlook ML’s potential as a research methodology for empirical research, which answers the central inquiry of theory building “Is it valid or true.” The up-to-date developments in statistical ML and model-agnostic interpretation have rendered the algorithms decent statistical and causal inference tools. For descriptive theory development reliant on association, recent works from authoritative management journals have begun using ML for pattern discovery and theory building. Nevertheless, they fall short of offering statistical tests for variable/interaction importance and integrating them with the extant techniques. For causality that facilitates normative theory, the econometrics literature has devised ML-based causal effect estimators but lacks empirical application in information and operations management research. To fill in the gaps, this dissertation comprises two essays. The first essay articulates the utilization of statistical ML for explanatory and confirmatory theory-building with associations. We propose an analysis protocol based on Random Forest (RF) for abductive and deductive theory building, equipped with statistical tests for the significance of variables and the interactions. The protocol helps uncover the patterns and test the association. Based on extensive simulations, we shed light on the context where MLs exhibit superiority and are complementary to the prevalently used econometrics model for empirical data analysis. For the second essay, we introduce Double Machine Learning (DML), a general framework of ML-based causal inference, for theory building. In addition to numerically assessing the statistical properties/robustness of DML, we use DML to empirically analyze human-system interaction in the context of semi-automated replenishment decision-making. Reliant on the proven statistical validity of DML for dynamic panel data, we uncover robust causal patterns for the contexts where managers’ interventions to the algorithmic decisions improve inventory performance. The two essays are dedicated to integrating, validating, and employing sophisticated ML techniques for theory building, and make contributions to the methodological literature on ML-based inference in management and the literature on empirical operations management and human-system interaction in decision-making. en_US dc.description.tableofcontents Section1 Introduction 1 Section2 Supervised Machine Learning for Theory Building and Testing: Opportunities in Operations Management 6 Section2.1 Introduction 6 Section2.2 Machine Learning in Empirical OM 9 Section2.3 Machine Learning for Theory Building 15 Section2.3.1 Pattern Discovery for Theorizing 15 Section2.3.2 Abductive Reasoning 22 Section2.4 Machine Learning for Theory Testing 24 Section2.4.1 Moving Beyond Linearity and Low-Dimensionality 24 Section2.4.2 Illustrations of RF for Theory Testing 26 Section2.4.2.1 CIs and Predictive Significance 27 Section2.4.2.2 Linear Hypothesis 28 Section2.4.2.3 Nonlinear Hypothesis 33 Section2.5 Extended Applications 37 Section2.6 Concluding Remarks 40 Section2.7 Appendix 43 Section2.7.1 Recent OM Studies with the Use of Supervised ML 43 Section2.7.2 Machine Learning Algorithms 45 Section2.7.3 Jackknife CIs and Permutation Test 47 Section2.7.3.1 Jackknife-After-Bootstrap and Infinitesimal Jackknife 47 Section2.7.3.2 Bias-Variance Analysis 49 Section2.7.3.3 Permutation Test with Random Forest 50 Section2.7.4 Linear Regression Modeling 52 Section3 Understanding Human-Algorithm Interaction in Ordering Decisions: A Computational Intelligence Approach 54 Section3.1 Introduction 54 Section3.2 Literature Review 58 Section3.3 Methodology 63 Section3.3.1 Causal Inference and Estimators 63 Section3.3.2 Double Machine Learning 64 Section3.3.3 Simulation Study 67 Section3.3.3.1 Cross-sectional Data and ATE 67 Section3.3.3.2 Longitudinal Data and CATE 70 Section3.4 Research Context 72 Section3.4.1 The Case Company 72 Section3.4.2 Outcome and Treatment 74 Section3.4.3 Contexts and Moderators 76 Section3.4.3.1 Inventory-related Contexts 77 Section3.4.3.2 Supplier-related Contexts 78 Section3.4.3.3 Demand-related Contexts 79 Section3.5 Empirical Analysis 80 Section3.5.1 Model and Pre-processing 80 Section3.5.2 Main Analysis 82 Section3.5.2.1 Inventory-related Contexts 85 Section3.5.2.2 Supplier-related Contexts 87 Section3.5.2.3 Demand-related Contexts 89 Section3.5.2.4 Robustness Check 90 Section3.6 Exploratory Theory Development 91 Section3.7 Discussion 95 Section3.7.1 Conclusions and Contributions 95 Section3.7.2 Limitations and Future Research 97 Section3.8 Appendix 100 Section3.8.1 Variables for DML Orthogonalization 100 Section3.8.2 Z-test for Robustness Test 101 References 102 zh_TW dc.format.extent 2376870 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356508 en_US dc.subject (關鍵詞) 理論建構 zh_TW dc.subject (關鍵詞) 統計機器學習 zh_TW dc.subject (關鍵詞) 雙重機器學習 zh_TW dc.subject (關鍵詞) 統計與因果推論 zh_TW dc.subject (關鍵詞) 人機互動 zh_TW dc.subject (關鍵詞) Theory building en_US dc.subject (關鍵詞) statistical machine learning en_US dc.subject (關鍵詞) double machine learning en_US dc.subject (關鍵詞) statistical and causal inference en_US dc.subject (關鍵詞) human-system interaction en_US dc.title (題名) 統計機器學習：理論建構與因果分析 zh_TW dc.title (題名) Statistical Machine Learning for Theory-building and Causal Analysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Agrawal, A., Gans, J., & Goldfarb, A. 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