Trailer
This is NOT a standard causal inference course.
It draws on a decade of global teaching and advisory experience to highlight key cues and concepts that are often overlooked, even by experts.
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"Quentin is the best expert in Causal AI I know. He elegantly combines a deep knowledge of all the subjects relating to statistics, AI and causality, with the most clear explanations for novices and experts alike. I still have his TED talk in mind in which he wrapped up very deep concepts of causality in one, enjoyable and actionable talk.""
Pr. Charles Ayoubi
Expert in technology (AI) and innovation, ESSEC Paris/Harvard Business School
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"Dr Gallea's training provides insights that are counterintuitive and very hard to find in other resources. I strongly recommend to anyone who wants to measure real impact and truly support decision making with data. I can highlight his clarity in explaining the distinction between statistical significance and causal importance, as well as the role of Double Machine Learning in estimating the effects of the one variables on each other."
Marcos Brum
Senior Data Scientist, AI Collaborator, Inc.
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"In the course, he clearly explained how combining causal inference with machine learning can lead to smarter, more transparent decision-making in business. His passion for the subject is evident, and he has a real talent for breaking down complex ideas with clarity and depth."
Amann Anand
Data Scientist, Ameriprise Financial Services
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"Quentin Gallea is an exceptionally knowledgeable and approachable expert in causal inference. His advice is clear and accessible while remaining rigorous and detailed, and he takes the time to genuinely engage with your questions and research context. His passion for doing causality properly is obvious throughout his work and interactions."
Carlo Maino
Scientific Collaborator, HES-SO
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"I recently took one of Dr. Quentin Gallea's trainings, 'From Prediction to Causation,' and was genuinely impressed. His deep expertise in causality clearly shows, and he explains the real power of causal thinking and how it differs from predictive models in a very clear way. He also highlights common pitfalls, challenges, and risks, helping to deconstruct many assumptions and misconceptions around the fundamentals. Clear, rigorous, and very practical, definitely worth it!"
Camilo Caceres
Data Science/ Machine Learning Expert - Staff Engineer, Mercado Libre
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"What surprises you with Quentin when he speaks about causality, is that under the accessible, seemingly simplistic language there is a wealth of deep seated competence and knowledge. Notwithstanding the fact that he is a masterclass in public speaking, concise, engaging and entertaining, he navigates the concepts with well explained examples."
Jeremie Diboine
Product Manager, ID Quantique
Overview of the course.
WHY?
Causal inference is essential across industries like online marketing, e-commerce, app optimization, and health, where impact matters more than correlation.
Causal inference is the most reliable way to evaluate the real business effect of ML/AI models in production (the quality of the prediction is not the right metric for this!).
Mastering causal skills helps you stand out in a crowded field where few master it and meet the growing demand for experts who can go beyond prediction.
You don't need to be experienced, just basic knowledge about predictive inference.
This course helps you start (or continue) your causal inference journey the right way. You'll learn to understand the foundational differences between predictive and causal inference, so you can avoid common mistakes and apply causal reasoning confidently in your work.
This is not a full methods course that covers every estimator or causal model in depth. However, I'll share my free guide on how to learn causal inference on your own and for free.
For this you can turn to my other course: Applied Causal Inference Masterclass
โ ML and AI professionals who want to understand causal inference correctly
โ Those who've started learning but feel stuck or confused by mixed terminology
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Curious data scientists seeking to bridge theory and application
โ People who expect an exhaustive overview of all causal methods (Check my other course for this: Applied Causal Inference Masterclass)
โ Those who want only coding tutorials without conceptual understanding