Our CEO, Dr Michael Haft, was recently featured on the Data Science Conversations podcast, where he discussed the growing importance of causal AI in modern data science.
In the episode, Dr. Haft explains why predictive models alone are no longer enough. While traditional machine learning can forecast outcomes, it often fails to reveal the underlying cause-and-effect relationships required for intelligent intervention.
A practical manufacturing example illustrates the difference: predictive analytics identified failing components, but causal discovery uncovered the root cause — enabling a targeted fix.
The conversation highlights why organizations must move from correlation to causality, especially in complex domains like manufacturing and healthcare. As AI systems increasingly support operational decisions, understanding “why” becomes essential.
Listen to the full episode to explore how Causal AI bridges the gap between prediction and actionable insight.