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Causal Interfaces

The interaction of two binary variables, assumed to be empirical observations, has three degrees of freedom when expressed as a matrix of frequencies. Usually, the size of causal influence of one variable on the other is calculated as a single value, as increase in recovery rate for a medical treatment, for example. We examine what is lost in this simplification, and propose using two interface constants to represent positive and negative implications separately. Given certain assumptions about non-causal outcomes, the set of resulting epistemologies is a continuum. We derive a variety of particular measures and contrast them with the one-dimensional index.

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Related contextAuthorshipTopic signalTopic signalTopic signalWCausal Interfacespreprint / 2014ADavid A. EubanksResearcherTArtificial Intelligence22915 worksTmath.ST3384 worksTStatistics Theory3281 works
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Causal Interfaces

preprint / 2014

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