English

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.

Keywords

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

R2 v1 2026-06-22T03:53:59.923Z