What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks.
What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks.
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DOI：
10.3390/e21050459
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发表时间：
2019-05-02
期刊：
影响因子：
--
通讯作者：
Tononi G
中科院分区：
文献类型：
--
作者：
Albantakis L;Marshall W;Hoel E;Tononi G
实际因果关系涉及的问题是：“什么导致了什么？”考虑一个由相互作用的元素组成的系统中的两个状态之间的转换，例如人工神经网络或生物脑回路。哪种突触组合导致神经元放电？哪些图像特征导致分类器误解图片？即使详细了解系统的因果网络，其元素，它们的状态，连接性和动态，也不能自动提供一个简单的答案，“什么导致了什么？”问题基于图形模型与系统干预相结合的实际因果关系的反事实描述，在解决特定问题案例方面取得了初步成功，符合直观的因果判断。在这里，我们从因果关系的一组基本要求（实现，组成，信息，集成和排除），并制定一个严格的，定量的实际因果关系，这是一般适用于离散动力系统。我们提出了一个正式的框架来评估这些基于系统干预和分区的因果要求，该框架考虑了状态转换的所有反事实。该框架通过识别和量化连接两个连续系统状态的所有实际原因和影响的强度来提供对过渡的完整因果解释。最后，我们研究了几个典型的案例和悖论的因果关系，并表明，他们可以照亮所提出的框架，量化实际的因果关系。
Actual causation is concerned with the question: “What caused what?” Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system’s causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the “what caused what?” question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation, that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements based on system interventions and partitions, which considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.
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影响因子：
4.1
作者：
Hoel, Erik P.;Albantakis, Larissa;Tononi, Giulio
通讯作者：
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影响因子：
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0.4
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3.4
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通讯作者：
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