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What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks

1
Department of Psychiatry, Wisconsin Institute for Sleep and Consciousness, University of Wisconsin-Madison, Madison, WI 53719, USA
2
Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
3
Allen Discovery Center, Tufts University, Medford, MA 02155, USA
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(5), 459; https://doi.org/10.3390/e21050459
Received: 21 February 2019 / Revised: 26 April 2019 / Accepted: 28 April 2019 / Published: 2 May 2019
(This article belongs to the Special Issue Integrated Information Theory)
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Abstract

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. View Full-Text
Keywords: graphical models; integrated information; counterfactuals; Markov condition graphical models; integrated information; counterfactuals; Markov condition
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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.

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