Causal Bandits Podcast

The Causal Bandits Podcast provides access to leading researchers and practitioners in causality and causal ML, delivering high-prestige conversations that attract ML/AI decision-makers. It’s a strong channel for PR targeting data scientists, researchers, and industry leaders shaping causal methods; booking favors clearly aligned expertise and established credentials.

34 episodes, Irregular, 4.8 rating

<1k, Male, USA

Causal Bandits Podcast with Alex Molak is here to help you learn about causality, causal AI and causal machine learning through the genius of others.&nbsp;The podcast focuses on causality from a number of different perspectives, finding common grounds between academia and industry, philosophy, theory and practice, and between different schools of thought, and traditions.&nbsp;Your host, Alex Molak is an a machine learning engineer, best-selling author, and an educator who decided to travel the world to record conversations with the most interesting minds in causality to share them with you.Enjoy and stay causal!Keywords: Causal AI, Causal Machine Learning, Causality, Causal Inference, Causal Discovery, Machine Learning, AI, Artificial Intelligence

Aleksander Molak

Technology, Science, Business, Education

PhD or senior-level researcher/engineer with active work in causality, AI, or ML; proven publication or leadership

major publications in causality/AI, leadership roles in top institutions/companies, foundations of causal analysis tools or methods (e.g., DoWhy), conference keynote or advisory roles

Mark van der Laan - TMLE, DML, Causal Roadmap, Uncertainty Quantification, Julia Rohrer - Causal Inference In Psychology, Reproducibility, Multiverse Analysis, Amit Sharma - Agents, Causal AI, Dowhy, Evaluation Of Causal Models, Eric J. Daza - N-of-1 Experiments, Single-subject Designs, Personalized Medicine, Ciarn Gilligan-Lee - Quantum Causal Models, Causal Inference At Spotify, Astro-physics Applications

causality, causal inference, causal AI, causal ML, TMLE, DML, DoWhy, n-of-1, causal graphs, reproducibility