Paul Hünermund

In this engaging discussion, Paul Hünermund, an assistant professor at Copenhagen Business School and expert in causal AI, dives into the nuances of causal inference in AI and data science. He clarifies how it differs from mere correlation and its application in business decision-making. Paul discusses methods like double machine learning and experimentation, highlights the gap between causal analysis and predictive models, and shares trends that underscore the importance of understanding causal relationships in enhancing AI methodologies.

Paul Hünermund, Assistant Professor at Copenhagen Business School, dives into the transformative world of causal AI in business. He explains how this technology surpasses traditional methods by focusing on cause and effect, which helps in tackling issues like gender pay disparities. The conversation highlights the significance of domain knowledge for effective causal inference, the crucial role of managers in implementing these strategies, and practical applications that can enhance decision-making across industries. Get ready to explore the future of AI-driven business strategies!

Paul Hünermund, assistant professor of Strategy and Innovation, discusses why the Nobel Prize in Economics was awarded to David Card, Joshua Angrist, and Guido Imbens. They explore the revolutionary framework of natural experiments and the practical implications of their research in labor markets, education, and business decision-making. The podcast also delves into the role of machine learning in causal analysis and invites feedback and subscription.