Measuring Causal Specificity

Griffiths, Paul E.; Pocheville, Arnaud; Calcott, Brett; Stotz, Karola; Kim, Hyun-Ju; Knight, Rob

doi:10.1086/682914

Cited by 79 publications

(107 citation statements)

References 17 publications

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“…Others have outlined similar metrics, such as the information-theoretic causal power in 2005 [11] and the information flow in 2008 [12], which was renamed "causal specificity" in 2015 [13]. However, here EI is used to refer to this general concept (the mutual information after interventions or perturbations) because of its historical precedence [10], along with its proven link to important properties of causal structure and the previous use of it to demonstrate causal emergence [3].…”

Section: Introductionmentioning

confidence: 99%

When the Map Is Better Than the Territory

Hoel

2017

Entropy

110

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Abstract:The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions may be useful to observers, they are at best a compressed description and at worse leave out critical information and causal relationships. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus and be more informative at a macroscale. That is, a macroscale description of a system (a map) can be more informative than a fully detailed microscale description of the system (the territory). This has been called "causal emergence." While causal emergence may at first seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different descriptions of those systems take advantage of that capacity to various degrees. For some systems, only macroscale descriptions use the full causal capacity. These macroscales can either be coarse-grains, or may leave variables and states out of the model (exogenous, or "black boxed") in various ways, which can improve the efficacy and informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel's capacity. The causal capacity of a system can approach the channel capacity as more and different kinds of macroscales are considered. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscale description.

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Section: Introductionmentioning

confidence: 99%

When the Map Is Better Than the Territory

Hoel

2017

Entropy

110

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“…), while assigning 0 to all other interventions (hence these intervention distributions capture the actual variation in X \X B and X A resp. in the sense of [13]). We refer to Eqs.…”

Section: Appendix a Discrete Cyclic Causal Systems And Causal Informamentioning

confidence: 99%

Cyclic and Multilevel Causation in Evolutionary Processes

Warrell

Gerstein

2019

Preprint

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Many models of evolution are implicitly causal processes. Features such as causal feedback between evolutionary variables and evolutionary processes acting at multiple levels, though, mean that conventional causal models miss important phenomena. We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearl's 'do'-calculus to incorporate cyclic causal interactions and multilevel causation. We also develop information-theoretic notions necessary to analyze causal information dynamics in our framework, introducing a causal generalization of the Partial Information Decomposition framework. We show how our causal framework helps to clarify conceptual issues in the contexts of complex trait analysis and cancer genetics, including assigning variation in an observed trait to genetic, epigenetic and environmental sources in the presence of epigenetic and environmental feedback processes, and variation in fitness to mutation processes in cancer using a multilevel causal model respectively, as well as relating causally-induced to observed variation in these variables via information theoretic bounds. In the process, we introduce a general class of multilevel causal evolutionary processes which connect evolutionary processes at multiple levels via coarse-graining relationships. Further, we show how a range of 'fitness models' can be formulated in our framework, as well as a causal analog of Price's equation (generalizing the probabilistic 'Rice equation'), clarifying the relationships between realized/probabilistic fitness and direct/indirect selection. Finally, we consider the potential relevance of our

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“…To avoid a lengthy digression, in this section I assume the reader is familiar with application of information theory to measuring causation (see Griffiths et al 2015). Griffiths and colleagues argue that range-of-influence specificity can be captured by measuring the mutual information between a cause variable and an effect variable, and call this measure :…”

Section: Bourrat's Many Measures Clarified and Upgradedmentioning

confidence: 99%

“…Step 1 is crucial here. For no matter how insightful the analysis of a model is, if that model does not represent the relevant biology, we have not made progress on the problem (Levy 2011 […] Woodward's radio is formally equivalent to the bimolecular example of transcription presented in Griffiths et al (2015). As such, Woodward's radio also has some biological relevance.…”

Section: On Models and Mischaracterisationmentioning

confidence: 99%

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Further clarification on permissive and instructive causes

Calcott

2019

Biol Philos

Self Cite

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I respond to recent criticism of my analysis of the permissive-instructive distinction and outline problems with the alternative analysis on offer. Amongst other problems, I argue that the use of formal measures is unclear and unmotivated, that the distinction is conflated with others that are not equivalent, and that no good reasons are provided for thinking the alternative model or formal measure tracks what biologists are interested in. I also clarify my own analysis where it has been misunderstood or ignored.

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Measuring Causal Specificity

Cited by 79 publications

References 17 publications

When the Map Is Better Than the Territory

When the Map Is Better Than the Territory

Cyclic and Multilevel Causation in Evolutionary Processes

Further clarification on permissive and instructive causes

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