Abstract
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.
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Cooper, G.F. A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships. Data Mining and Knowledge Discovery 1, 203–224 (1997). https://doi.org/10.1023/A:1009787925236
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DOI: https://doi.org/10.1023/A:1009787925236