A new causal power theory

Korb, Kevin B.; Nyberg, Erik; Hope, Lucas R.

doi:10.1093/acprof:oso/9780199574131.003.0030

Cited by 14 publications

(22 citation statements)

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“…However, transfer entropy still relies on observational data, while a causal approach generally requires perturbational data [ 53 ] (although observational data is sufficient for causal inference in ER systems since they cycle through all their possible states). In this way, our approach is more closely related to proposed measures of causal information flow [ 44 , 54 ], but evaluated in a state-dependent manner, as the information specified by the subset in its current state about its causes and effects (see also [ 55 ]).…”

Section: Discussionmentioning

confidence: 99%

Causal Composition: Structural Differences among Dynamically Equivalent Systems

Albantakis

Tononi

2019

Entropy

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The dynamical evolution of a system of interacting elements can be predicted in terms of its elementary constituents and their interactions, or in terms of the system’s global state transitions. For this reason, systems with equivalent global dynamics are often taken to be equivalent for all relevant purposes. Nevertheless, such systems may still vary in their causal composition—the way mechanisms within the system specify causes and effects over different subsets of system elements. We demonstrate this point based on a set of small discrete dynamical systems with reversible dynamics that cycle through all their possible states. Our analysis elucidates the role of composition within the formal framework of integrated information theory. We show that the global dynamical and information-theoretic capacities of reversible systems can be maximal even though they may differ, quantitatively and qualitatively, in the information that their various subsets specify about each other (intrinsic information). This can be the case even for a system and its time-reversed equivalent. Due to differences in their causal composition, two systems with equivalent global dynamics may still differ in their capacity for autonomy, agency, and phenomenology.

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Section: Discussionmentioning

confidence: 99%

Causal Composition: Structural Differences among Dynamically Equivalent Systems

Albantakis

Tononi

2019

Entropy

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“…Occurrences

that lower the probability of a subsequent occurrence

have been termed “preventative causes” by some [ 33 ]. Rather than counting a negative effect information

as indicating a possible “preventative effect”, we take the stance that such an occurrence

has no effect on

, since it actually predicts other occurrences

that did not happen.…”

Section: Theorymentioning

confidence: 99%

“…In this context, the main difference between our proposed framework and existing “contingency”-based definitions is that we simultaneously consider all counterfactual states of the transition, rather than a single contingency (e.g., as in [ 8 , 11 , 19 , 20 , 21 , 30 , 31 ]). This allows us to express the causal analysis in probabilistic, informational terms [ 25 , 32 , 33 , 34 ], which has the additional benefit that our framework naturally extends from deterministic to probabilistic causal networks, and also from binary to multi-valued variables. Finally, it allows us to quantify the strength of all causal links between occurrences and their causes and effects within the transition.…”

Section: Introductionmentioning

confidence: 99%

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

Albantakis

Marshall

Hoel

et al. 2019

Entropy

123

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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 that is based on system interventions and partitions, and 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.MSC 2010 subject classifications: Primary 62-09; secondary 60-J10.

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“…I call this the Proportionality Principle. 8 It is not difficult to prove that Multiplicativity 7 Incidentally, this condition assumes transitivity of causation, which is warranted for C = ¬C (Korb et al, 2011;Halpern, 2016…”

Section: Argument 2: the Multiplicativity Principlementioning

confidence: 99%

“…Principled proposals for explicating causal strength are rare and spread over different disciplines, each with their own motivation and intended context of application. This includes cognitive psychology (Cheng, 1997;Icard et al, 2017), computer science and machine learning (Pearl, 2000;Korb et al, 2009Korb et al, , 2011, statistics (Good, 1961a,b;Holland, 1986;Cohen, 1988), epidemiology and clinical medicine (Poole, 2010;Broadbent, 2013), philosophy of science (Suppes, 1970;Eells, 1991), political philosophy and social choice theory (Braham and van Hees, 2009), and legal theory (Rizzo and Arnold, 1980;Hart and Honoré, 1985;Kaiserman, 2017). Although these approaches use a common formalism-probability theory-, the proposed explications differ substantially (see the survey of Fitelson and Hitchcock, 2011).…”

Section: Introductionmentioning

confidence: 99%

Foundations of a Probabilistic Theory of Causal Strength

Sprenger

2018

The Philosophical Review

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This paper develops axiomatic foundations for a probabilistic theory of causal strength as difference-making. I proceed in three steps: First, I motivate the choice of causal Bayes nets as an adequate framework for defining and comparing measures of causal strength. Second, I prove several representation theorems for probabilistic measures of causal strengththat is, I demonstrate how these measures can be derived from a set of plausible adequacy conditions. Third, I use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. I conclude by discussing my results and outlining future research avenues. * Forthcoming in The Philosophical Review. The proofs of the theorems are contained in the supplemental material.

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A new causal power theory

Cited by 14 publications

References 0 publications

Causal Composition: Structural Differences among Dynamically Equivalent Systems

Causal Composition: Structural Differences among Dynamically Equivalent Systems

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

Foundations of a Probabilistic Theory of Causal Strength

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