An algorithm for direct causal learning of influences on patient outcomes
Conclusion Our results show that DCL outperforms FGS, PC, CPC, and FCI in almost every case, demonstrating its potential to advance causal learning. Furthermore, our DCL algorithm effectively identifies direct causes in the LOAD and Metabric GWAS datasets, which indicates its potential for clinical applications.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research
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