Open AccessMining for Causal Regularities
Author(s) -
Thomas Bidinger,
Hannah Buzard,
James Hearne,
Amber Meinke,
Steven F. Tanner
Publication year - 2021
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/5xls
Subject(s) - computer science , set (abstract data type) , causal model , set theory , causal structure , data mining , artificial intelligence , theoretical computer science , mathematics , statistics , programming language , physics , quantum mechanics
This paper reports on an algorithmic exploration of the theory of causal regularity based on Mackie’s theory of causes as MINUS conditions, i.e., minimal insufficient but necessary member of a set of conditions that, though unnecessary, are sufficient for the effect. We describe the algorithm to extract causal hypotheses according to this model and the results of its application to a number of real world data sets. Results suggest further promising applications, modifications and extensions that might derive further insights of a dataset.