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Data Science ConversationsFeaturing the latest developments in Data Science & AI research Author: Damien Deighan and Philipp Diesinger
Welcome to the Data Science Conversations Podcast hosted by Damien Deighan and Dr Philipp Diesinger. We bring you interesting conversations with the worlds leading Academics working on cutting edge topics with potential for real world impact. We explore how their latest research in Data Science and AI could scale into broader industry applications, so you can expand your knowledge and grow your career. Every 4 or 5 episodes we will feature an industry trailblazer from a strong academic background who has applied research effectively in the real world. Podcast Website: www.datascienceconversations.com Language: en Genres: Business, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it Trailer: |
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Understanding Cause and Effect: Is Causal Discovery The Missing Layer in Artificial Intelligence?
Episode 32
Wednesday, 11 February, 2026
Michael Haft, founder of xplain Data, discusses causal discovery and causal AI, explaining how understanding cause-and-effect relationships goes beyond predictive modeling to enable truly intelligent interventions. He explores the technical foundations of object analytics, real-world applications in healthcare and manufacturing, and his vision for integrating causal AI into future intelligent systems.Episode Summary Causal Discovery vs. Prediction - Causal discovery aims to understand why things happen rather than just predicting what will happen. Unlike predictive models that rely on correlations, causal discovery identifies true cause-and-effect relationships necessary for intelligent interventions and goal achievement.The Confounder Challenge - Understanding causality requires comprehensive data to identify confounders—hidden common causes that create spurious correlations. The gray hair and glasses example illustrates how age acts as a confounder, making the two correlated without a direct causal relationship between them.Object Analytics Technology - Traditional machine learning requires flat tables, but real-world data (like electronic health records with 150+ tables) is inherently complex. Object analytics allows algorithms to work with comprehensive, holistic data structures, enabling deeper causal analysis without manual feature engineering.Manufacturing Use Case - A cylinder head manufacturing example demonstrates how causal discovery identified the complete pathway from washing machine timing through part temperature to false negative leakage test results, enabling an intelligent process intervention that traditional predictive models couldn't provide.Healthcare Applications - Projects using MIMIC hospital data analyze causes of pressure injuries in patients. The vision is to provide doctors with causal knowledge derived from millions of patient records to improve treatment decisions, discover new drug effects, and enable cost-efficient healthcare.Path to Causal Maturity - Organizations need education on the difference between prediction and causality, comprehensive data availability, and engagement from both business owners (who have problems to solve) and data science teams. The shift requires iterative learning and hands-on experience with the technology.Community Edition Launch - Explained Data is releasing a community edition starting with pre-configured object analytics models for the MIMIC healthcare dataset, followed by a full version for the broader data science community, with free access for universities and evaluation purposes.Future of Causal AI - The next generation of AI systems will integrate causal layers with large language models, moving beyond text rephrasing to answering "why" questions based on empirical cause-and-effect relationships, particularly transforming healthcare and enabling more explainable, intelligent decision-making systems.
