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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.Comment: 20 pages, 3 figure
Identifiability and transportability in dynamic causal networks
In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks.
We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the identification
procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure
for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain.Preprin
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