From Prediction to Causation:
Shift your mindset, acquire new tools and elevate your career.
Elevate your career with causality today!
What you will get with this class?
✅ Differentiate your profile, Deliver business impact and Meet the rising industry demand
✅ Know how and when to use causal inference
✅ Understand why you need causal inference to evaluate AI (ML) models just as top tech leaders are doing (e.g. Meta)
✅ Avoid the most common traps when trying to “do causality” with standard AI or Machine Learning thinking.
✅ Varied applications examples from the industry
Package Pro
💲125USD (satisfied or refunded)
Package Premium
💲250USD (satisfied or refunded)
📅No Next cohort for the moment as I focus on the bigger class: Applied Causal Inference Masterclass with Matheus Facure
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.
What You'll Learn in the Course
Part I – Foundations
📖 Predictive inference vs causal inference: Know the difference, and when to use each.
📖 How to combine both types of inference in a powerful collaboration.
📖 How to frame a causal question using Directed Graphs.
📖 Which features should be included or excluded from a causal model
📖 Why correlation does not imply causation (endogeneity).
Part II – Common Mistakes
📖 Strong predictive power ≠ causal effect
📖 Explainability / interpretability ≠ causality
📖 Low statistical significance ≠ "this variable doesn't matter"
📖 Predictive power is not the goal in causal inference
Part III – How to do it right
📖 Causal model evaluation
📖 Causality with controls
📖 Double Machine Learning introduction
📖 What's next?
Why Causality Is the Next Big Skill for ML Practitioners
🏅 Differentiate your profile
Causal inference is essential across industries like online marketing, e-commerce, app optimization, and health, where impact matters more than correlation.
⚡ Deliver business impact
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!). Use the same methods as top tech leaders!
📈 Meet the rising industry demand
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.
Is this for you?
Pre-requisite: You don't need to be experienced, just basic knowledge about predictive inference. You need to know what is a p-value, and a linear regression.
✔️ What it is
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.
Who it's for
✔️ ML and AI professionals who want to understand causal inference correctly
✔️ Those who've started learning but feel stuck or confused by mixed terminology
✔️ Curious data scientists seeking to bridge theory and application
❌ What it isn't
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.
About the Author: Quentin Gallea Ph.D
Dr. Quentin Gallea combines academic rigor with real-world impact to help professionals move from prediction to causation.
✅ Delivered workshops for billion-dollar companies (including Google)
✅ Advised C-suites and data leaders worldwide on causal inference and AI impact
✅ Trained 15,000+ students and professionals across industries
✅ Published research in top scientific journals
✅ Speaker at leading international events, including:
TEDx
Causal Data Science Meeting
Applied Machine Learning Days (forthcoming)
National Association for Business Economics (forthcoming)
✅ Author of The Causal Mindset Handbook (forthcoming)
✅ Known for making complex AI and causal concepts accessible without sacrificing rigor
Selected non-technical articles:
📄 How 'causal' AI can improve your decision-making
📄 Why Machine Learning Is Not Made for Causal Estimation
More information about the Quentin here: quentingallea.com
Testimonials
★★★★★
"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.
★★★★★
"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.
★★★★★
"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."
Pr. Charles Ayoubi
Expert in technology (AI) and innovation, ESSEC Paris/Harvard Business School
★★★★★
"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
★★★★★
"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
★★★★★
"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
