Graphs for Causal AI
Data Skeptic
00:00
Navigating Causality in Knowledge Graphs
This chapter explores the crucial role of Markov-based splits in causal networks to prevent data leakage and maintain the integrity of link prediction tasks. It highlights the importance of accurate causal reasoning, methods for evaluating predictive performance, and the transformation of datasets for testing. Additionally, it addresses the challenges of causal discovery and the application of causal knowledge graphs across various industries, emphasizing the need for interdisciplinary collaboration.
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