杠杆(统计)
计算机科学
实证研究
推论
解释力
数据科学
现存分类群
统计假设检验
机器学习
人工智能
管理科学
认识论
数学
经济
统计
哲学
生物
进化生物学
作者
Yen‐Chun Chou,Howard Hao‐Chun Chuang,Ping Yen Chou,Rogelio Oliva
摘要
Abstract Machine learning's (ML's) unique power to approximate functions and identify non‐obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem‐solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well‐developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.
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