Applied Causal Inference Masterclass
From identification to business impact. A 16-week cohort with
Dr. Quentin Gallea and Matheus Facure.
Elevate your career with causality today!
What you will get with this class?
✅ Master the full causal inference toolkit, from identification to implementation for business.
✅ Go beyond tutorials with 16 weeks of structured content, hands-on notebooks, Whatsapp Group for questions and live debrief sessions with instructors.
✅ Applicable business insights from years of practice in industry/advisory by the two instructors
✅ Join a small, curated cohort (40 participants max) and build a lasting professional network through our WhatsApp community.
Pre-sale: Secure your seat
💲1200USD total fee
📅Starts the 1st of September
This is NOT a standard causal inference course.
Built for practitioners, not academics.
It is a 16-week applied masterclass, co-taught by two practitioners who combine rigorous methodology with industry-tested workflows. You will not just watch videos. You will run notebooks, debate your design choices with peers, and get direct feedback from both instructors in live sessions.
What You'll Learn in the Course
Block 1 – Foundations
📖 Causal vs. predictive inference
📖 Directed Acyclic Graphs (DAGs)
📖 A word on causal discovery.
Block 2 – Randomized Experiments
📖 Designing reliable experiments
📖 Avoiding common traps
📖 Analyzing the result
Block 3 – Controls and DoubleML
📖 Identification under unconfoundedness.
📖 Good and Bad controls
📖 Double Machine Learning (DML)
Block 4 – Panel Data
📖 Fixed effects model
📖 Controlling for unobserved factors
📖 Strength and limitations
Block 5 – Difference-in-Difference
📖 Canonical model
📖 Advanced models
📖 Event Study
Block 6 – Regression discontinuity
📖 Sharp RD
📖 Fuzzy RD
📖 Robustness
Block 7 – Instrumental Variable
📖 IV framework
📖 Assumptions and validation
📖 IV Examples in business
Block 8 – Refining Results
📖 Sensitivity Analysis
📖 CATE/HTE estimation (Heterogenous Treatment Effects)
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: Basic comfort with predictive modeling and Python. You should know what a p-value, t-stats, a linear regression, and hypothesis testing are. We will not start from zero, but we will not assume PhD-level econometrics either.
✔️ What it is
A 16-week premium cohort that takes you from causal foundations to the methods used by top tech teams. You will leave with the conceptual clarity to choose the right design for any business problem, and the applied skills to deliver on it.
Who it's for
✔️ Data scientists and ML engineers who need to measure impact.
✔️ Applied researchers and economists who want to bridge methodology and applied business work.
✔️ Practitioners who have started learning causal inference but feel stuck with academic resources not applicable enough in the business world
❌ What it isn't
This is not a beginner crash course on statistics, nor a passive video library. It is an active, cohort-based experience that requires you to engage, run notebooks, and show up to the live sessions.
About the Instructors
Matheus Facure
Matheus Facure was an economist and Senior Data Scientist at Nubank, the biggest FinTech company outside Asia, where he applies causal inference to real business decisions at scale, across credit, pricing, marketing, and cross-sell.
Applied causal methods to million-customer-scale business problems at Nubank, including automated credit and interest decisioning, marketing budget optimization, and cross-sell.
Author of Causal Inference for the Brave and True (open-source, widely used in the practitioner community).
Author of Causal Inference in Python: Applying Causal Inference in the Tech Industry.
Quentin Gallea Ph.D
Quentin Gallea, PhD, is an economist, educator, and author working at the intersection of causal inference, data science, and business strategy.
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, National Association for Business Economics
Author of The Causal Mindset Handbook
Organizer of The Causal Summit
