I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.

中文翻译：

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激进经验主义与机器学习研究

我从三个方面对数据科学的“数据拟合”与“数据解释”方法进行了对比：权宜，透明和可解释性。“数据拟合”是由这样一种信念驱动的，即理性决策的秘诀在于数据本身。相反，数据解释学校并不将数据视为唯一的知识来源，而是将其视为解释现实的辅助手段，而“现实”则代表生成数据的过程。我主张在因果逻辑的指导下，通过任务依赖的拟合与解释共生来恢复与数据科学的平衡。