Mining for Causal Regularities

14 pages•Published: November 2, 2021

Thomas Bidinger, Hannah Buzard, James Hearne, Amber Meinke and Steven Tanner

Abstract

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.

Keyphrases: causal regularity, data mining, inus condition, mill s methods, minus condition

In: Yan Shi, Gongzhu Hu, Quan Yuan and Takaaki Goto (editors). Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering, vol 79, pages 40-53.

Links:	https://easychair.org/publications/paper/K8j7
	https://doi.org/10.29007/5xls

BibTeX entry

@inproceedings{CAINE2021:Mining_Causal_Regularities,
  author    = {Thomas Bidinger and Hannah Buzard and James Hearne and Amber Meinke and Steven Tanner},
  title     = {Mining for Causal Regularities},
  booktitle = {Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Quan Yuan and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {79},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/K8j7},
  doi       = {10.29007/5xls},
  pages     = {40-53},
  year      = {2021}}

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