Abstract
One of the most significant challenges for machine learning nowadays is the discovery of causal relationships from data. This causal discovery is commonly performed using Bayesian like algorithms. However, more recently, more and more causal discovery algorithms have appeared that do not fall into this category. In this paper, we present a new algorithm that explores global causal association rules with Uncertainty Coefficient. Our algorithm, CRPA-UC, is a global structure discovery approach that combines the advantages of association mining with causal discovery and can be applied to binary and non-binary discrete data. This approach was compared to the PC algorithm using several well-known data sets, using several metrics.
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Notes
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Two variables are directly associated if a statistical test (for example
) finds them associated. - 2.
The data set is available in https://tinyurl.com/gitbub.
- 3.
Data set with 10 000 instances generated based on the network available in http://www.bnlearn.com/.
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) finds them associated.References
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Acknowledgments
This research was carried out in the context of the project FailStopper (DSAIPA/DS/0086/2018) and supported by the Fundação para a Ciência e Tecnologia (FCT), Portugal for the PhD Grant SFRH/BD/146197/2019.
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Nogueira, A.R., Ferreira, C., Gama, J., Pinto, A. (2021). Generalised Partial Association in Causal Rules Discovery. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_38
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