扎根理论的归纳逻辑和避免理论先入为主的原则与传统定量研究的演绎逻辑和假说检验大相径庭。在回顾传统定量研究理论生产局限的基础上，本文提出一种以定量方式直接助产理论的“计算扎根”方法：借助机器学习和归因算法，按照因果是可预测性的充分不必要条件之原理，根据对因变量的预测力筛选出以往研究未曾关注的自变量，以提出新的理论假说。本文对计算扎根的基本思路、逻辑前提、方法基础进行了系统阐述，并基于实际案例进行了演示。该方法弥补了定量研究理论生产的不足，在理论、学科、知识体系和社会治理等方面具有重要价值。

The inductive logic of grounded theory and the principle of avoiding theoretical preconceptions are significantly different from the deductive logic and hypothesis testing of traditional quantitative research. Based on a reflection on the limitations of theory production in quantitative research, this paper proposes a "Computing Grounded Theory (CGT)” approach that directly assists theories in a quantitative manner: With the help of machine learning and attribution algorithms, CGT identifies variables that have not been the focus of previous studies based on the predictive power of the independent variables in order to propose new theoretical hypotheses, following the principle that causality is a sufficient and unnecessary condition for predictability. This paper systematically discusses the basic idea, logical premise, and methodological foundation of CGT, while also providing an empirical example. The method bridges the gap in theoretical production of quantitative research and is of great value in theory, discipline, knowledge system and social governance.

陈茁、陈云松

本文为国家社会科学基金重大项目“大数据驱动的网络社会心态发展规律与引导策略研究”（19ZDA149）阶段性成果。

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