Causal Interfaces
Artificial Intelligence
2014-04-22 v1 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1404.4884,
title = {Causal Interfaces},
author = {David A. Eubanks},
journal= {arXiv preprint arXiv:1404.4884},
year = {2014}
}
Comments
20 pages, 3 figures