Causal Interfaces

04/18/2014
by   David A. Eubanks, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/16/2023

BISCUIT: Causal Representation Learning from Binary Interactions

Identifying the causal variables of an environment and how to intervene ...
research
07/15/2021

Obtaining Causal Information by Merging Datasets with MAXENT

The investigation of the question "which treatment has a causal effect o...
research
07/11/2012

Robustness of Causal Claims

A causal claim is any assertion that invokes causal relationships betwee...
research
10/24/2022

Rejoinder to discussions on "Instrumental variable estimation of the causal hazard ratio"

We respond to comments on our paper, titled "Instrumental variable estim...
research
08/10/2020

Using Multiple Imputation to Classify Potential Outcomes Subgroups

With medical tests becoming increasingly available, concerns about over-...
research
07/01/2020

Causal inference and constructed measures: towards a new model of measurement for psychosocial constructs

Psychosocial constructs can only be assessed indirectly, and measures ar...

Please sign up or login with your details

Forgot password? Click here to reset