1Department of Philosophy, Carnegie Mellon University 2Departments of Philosophy and Psychology, Carnegie Mellon University Correspondence David Danks, Departments of Philosophy and Psychology, Carnegie Mellon University, Pittsburgh, USA. Email: ddanks@cmu.edu Abstract Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger “pipeline” within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure.

中文翻译：

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因果发现算法：实用指南

1卡内基梅隆大学哲学系2卡内基梅隆大学哲学系函授David Danks，美国匹兹堡卡内基梅隆大学哲学系和心理学系。电子邮件：ddanks@cmu.edu摘要对世界的许多研究，包括哲学的研究，都旨在发现因果知识，并且已经开发了许多实验方法来辅助因果发现。最近，出现了一些算法，这些算法也可以从纯粹或主要是观测数据以及实验数据中学习因果结构。这些方法已开始应用于各种哲学背景，例如关于我们的自由意志和决定论概念的辩论。本文提供了有关这些方法的“用户指南”，尽管不是在软件包中指定确切的按钮按下的意义。相反，我们解释了在其中使用这些方法的更大的“管道”，并讨论了从最初的研究构想转向经过验证的因果结构的关键步骤。