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Online Courses

Courses are structured in a progressive order, moving from introductory intuition to theoretical foundations and then to applied estimation workflows. The sequence below represents the recommended learning order:

  1. Causal AI in a Nutshell

  2. Causal AI: A Theoretical Introduction

  3. Causal Inference with Linear Regression: A Modern Approach (Part I)

  4. Causal Inference with Linear Regression: A Modern Approach (Part II)

At the same time, courses can also be taken individually depending on prior experience or learning goals. Make sure to check the course prerequisites beforehand to confirm you have the necessary background knowledge.

Individual Courses

  • Free

Causal AI in a Nutshell

  • Course
  • 27 Lessons

A free course providing a clear introduction to Causal AI and its role in data-driven decision-making. Learn how causal approaches differ from predictive approaches and when causal analysis is needed in practice.
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  • €59

Causal AI: A Theoretical Introduction

  • Course
  • 64 Lessons

A theoretical introduction to causal inference focused on the foundations of the Pearlian framework. Learn what causal inference is about, how causal quantities are defined, under which assumptions they can be estimated from observational data, and how the Pearlian framework can be used to structure causal analyses.
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  • €99

Causal Inference with Linear Regression: A Modern Approach (Part I)

  • Course
  • 97 Lessons

Learn how linear regression can be used as an estimation tool in causal inference. Understand what regression coefficients represent, under which assumptions they can be interpreted as causal effects, how violations of unconfoundedness affect results, and how to use robustness tests and sensitivity analysis to assess and cope with these violations.
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  • €999

Causal Inference with Linear Regression: A Modern Approach (Part II)

  • Course
  • 112 Lessons

An applied course on causal inference using modern linear regression approaches, focused on real-world complexity. Learn how to design studies, construct realistic causal graphs, map them to adjustment strategies under realistic data constraints, diagnose conditions like positivity, and estimate (heterogeneous) causal effects using OLS and double machine learning, demonstrated through an end-to-end case study.
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Bundles

  • €329

Complete Causal Inference Program

  • Bundle
  • 4 Products

This bundle combines all courses and is designed to build a deep understanding of causal inference, covering theoretical foundations such as the ladder of causation, do-calculus, and structural causal models, alongside applied workflows including study design, positivity diagnostics, ATE and CATE estimation using double machine learning, and sensitivity analysis to unobserved confounding.
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  • €289

Causal Inference with Linear Regression

  • Bundle
  • 2 Products

This bundle combines Part I and Part II of the linear regression course series and is designed to build a deep understanding of causal inference with linear regression, covering both theoretical foundations such as graph theory and linear structural causal models, and applied workflows including sensitivity analysis to unobserved confounding, positivity diagnostics and double machine learning based on LASSO and OLS.
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