The paradigmatic methods of scientific investigation have been the control-experimental or “intervention” methods. Although not without their epistemic concerns, both in principle and in practice, the methods have nonetheless been thought to constitute the ideal of scientific investigation. Certain recent data-science practices employ methods that resemble and differ from these methods in a number of important ways. In this article, my concern will be with some of the ways in which the methods in these practices differ from control-experimental methods and with the epistemic implications these might be thought to have. I argue that, for most points of epistemic difference between the methods, any implied epistemic difference is superficial and that data-scientific methods can be understood to involve an implicit epistemic relation identical to that of control-experimental methods. Towards the end of the article, I examine a point of difference between data-scientific and control-experimental methods that might, in a subset of cases, be thought to have genuine epistemic significance. This point of difference is in the machine-learning techniques employed in the former, some of which techniques have implications, not for the mentioned epistemic relation as such, but for the possible content of the claim established by the investigation.

科学调查的典型方法是控制实验或“干预”方法。尽管在原则和实践中并非没有他们的认知关注，但这些方法仍然被认为构成了科学调查的理想。某些最近的数据科学实践在许多重要方面采用了与这些方法相似和不同的方法。在本文中，我将关注这些实践中的方法与控制实验方法的一些不同之处，以及这些可能被认为具有的认知含义。我认为，对于方法之间的大多数认知差异点，任何隐含的认知差异都是表面的，可以将数据科学方法理解为包含与控制实验方法相同的隐含认知关系。在文章的结尾，我检查了数据科学方法和控制实验方法之间的一个差异点，在一些情况下，这些差异可能被认为具有真正的认知意义。这个不同点在于前者采用的机器学习技术，其中一些技术具有影响，不是对于所提到的认知关系本身，而是对于调查确定的主张的可能内容。被认为具有真正的认识论意义。这个不同点在于前者采用的机器学习技术，其中一些技术具有影响，不是对于所提到的认知关系本身，而是对于调查确定的主张的可能内容。被认为具有真正的认识论意义。这个不同点在于前者采用的机器学习技术，其中一些技术具有影响，不是对于所提到的认知关系本身，而是对于调查确定的主张的可能内容。