A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There...

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Veröffentlicht in:Data mining and knowledge discovery Jg. 1; H. 2; S. 203 - 224
1. Verfasser: Cooper, Gregory F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer Nature B.V 01.01.1997
Schlagworte:
Algorithms
Data mining
Experiments
Pain
Probability
Probability distribution
Theory of constraints
Values
Variables
ISSN:1384-5810, 1573-756X
Online-Zugang:Volltext
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Zusammenfassung:This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1384-5810
1573-756X
DOI:10.1023/A:1009787925236