Mechanisms, Capacities, and Dependencies: A New Theory of Causal Reasoning

因果认知赋予人类令人印象深刻的能力。它允许我们预测和解释，计划行动，理解困惑，对转移情况进行有根据的猜测，思考反事实的替代方案，以及使用和设计创新设备。 目前没有一种心理学理论能够在一个综合的框架内对所有这些能力提供一个完整的解释。此外，侧重于特定任务的部分理论往往忽视了能力之间的相互作用。另一个缺点是，大多数理论忽略或淡化背景知识的重要作用的机制。在过去的几年里，在哲学和计算机科学中提出了更先进的理论，承认其中的一些缺陷。一个最近的发展是珍珠的因果关系理论。珀尔改变了他早先的观点，即因果关系可以归结为协变。他现在认为，抽象的结构因果模型编码未观察到的机制，在功能依赖性指导因果查询。然而，将机制建模为依赖关系已经受到了所谓的新机制观点的哲学家的批评，该观点已经被开发用于分析各种领域中的机制表示。例如，哲学家卡特赖特认为，自然科学中的统计依赖性是在所谓的法则机器的背景下发现的。法则机器，如钟摆，是由具有特定能力的互连组件组成的非因果装置。其他突出的例子是人工制品，如汽车或机器人。伪影的目的是产生协变。这些可以用贝叶斯网络来表示，但是为了提供因果理解，有必要理解协变是如何由底层的法则机器产生的。这两个框架，珍珠的理论和新机制的方法，都有局限性：而珍珠的理论只提供了一个贫穷的模型，机制知识的功能依赖变量之间的关系，新机制的观点夸大了外行人知道什么。此外，一个正式的理论集成的依赖知识和机制表示是缺乏这两种方法。一个新的理论（MEC-DEP）将被开发，解释因果认知的广度和灵活性作为一个产品的机制和依赖知识之间的相互作用。MEC-DEP将通过调查不同内容领域和人群（成人，儿童，非人类灵长类动物）中的不同因果模型，机制和任务进行实验测试。此外，将开发MEC-DEP的计算模型，并分析机制在心理学研究中的作用。新理论将从根本上改变我们对因果关系的思考，在许多领域，可以从更深入地理解因果推理的规范性和描述性基础中获益。

Causal cognition endows humans with impressive abilities. It allows us to predict and explain, plan actions, understand confoundings, make educated guesses about transfer situations, think about counterfactual alternatives, and use and engineer innovative devices. No present psychological theory can provide a complete account of all these competencies within an integrated framework. Moreover, partial theories focusing on specific tasks tend to neglect interactions between competencies. Another shortcoming is that most theories ignore or downplay the important role of background knowledge about mechanisms. In the past years, more advanced theories have been proposed in philosophy and computer science that acknowledge some of these deficits. One recent development is Pearl's theory of causation. Pearl has changed his earlier view that causation can be reduced to covariations. He argues now that abstract structural causal models encoding unobserved mechanisms in terms of functional dependencies guide causal queries. However, modeling mechanisms as dependencies has been criticized by philosophers working on the so-called New Mechanism view, which has been developed to analyze mechanism representations in a variety of domains. For example, the philosopher Cartwright has argued that statistical dependencies in the natural sciences are discovered in the context of so-called nomological machines. Nomological machines, such as a pendulum, are non-causal devices that consist of interconnected components with specific capacities. Other salient examples are artifacts, such as cars or robots. Artifacts are designed to produce covariations. These can be represented by a Bayes net, but to provide a causal understanding, it is necessary to understand how the covariations are generated by the underlying nomological machine. Both frameworks, Pearl's theory and the New Mechanism approach, have limitations: Whereas Pearl's theory only provides an impoverished model of mechanism knowledge in terms of functional dependencies between variables, the New Mechanism view exaggerates what laypeople know. Moreover, a formal theory integrates dependency knowledge and mechanism representations is lacking within both approaches. A new theory (MEC-DEP) will be developed that explains the breadth and flexibility of causal cognition as a product of the interaction between mechanism and dependency knowledge. MEC-DEP will be experimentally tested by investigating different causal models, mechanisms, and tasks in various content domains and populations (adults, children, non-human primates). Additionally, computational models of MEC-DEP will be developed and the role of mechanisms in psychological research will be analyzed. The new theory will fundamentally change our thinking about causality in the many areas that can profit from a deeper understanding of the normative and descriptive basis of causal inferences.