What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks
AbstractActual 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. View Full-Text
Share & Cite This Article
Albantakis, L.; Marshall, W.; Hoel, E.; Tononi, G. What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks. Entropy 2019, 21, 459.
Albantakis L, Marshall W, Hoel E, Tononi G. What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks. Entropy. 2019; 21(5):459.Chicago/Turabian Style
Albantakis, Larissa; Marshall, William; Hoel, Erik; Tononi, Giulio. 2019. "What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks." Entropy 21, no. 5: 459.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.