Causal Inference Courses and Certifications

Causal Diagrams: Draw Your Assumptions Before Your Conclusions

26 ratings at edX

Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.

• edX
• 9 weeks, 2-3 hours a week
• Self-Paced
• Free Online Course (Audit)

Coursera

Uncover Causal Impacts Fast

Estimate causal effects in business data: apply propensity score matching, test ignorability and overlap, run PC/FCI causal discovery, and check stability with bootstrap resampling.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Neso Academy

Basic Signal Properties - Signals and Systems Chapter 4

Master fundamental system properties including causality, linearity, time-invariance, stability, and invertibility through theory and GATE-level problem solving.

• YouTube
• 4 hours 36 minutes
• Self-Paced
• Free Video

教育定量研究方法（高级）

Advanced causal inference methods for evaluating educational research designs, from instrumental variables and randomized experiments to regression discontinuity and matching.

• XuetangX
• Self-Paced
• Free Online Course

Estimate treatment effects when data is incomplete, using regression discontinuity design and instrumental variables, with a project on weather systems and transit times.

• Codecademy
• 1 hour
• Self-Paced
• Free Trial Available

Coursera Career Certificate

AI Techniques, Causal Inference & Business Optimization

Build and evaluate retrieval-augmented conversational AI, explain black-box models, optimize with linear programming, genetic algorithms and reinforcement learning, and estimate business impact using causal inference.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 11 courses

Richard McElreath

Statistical Rethinking 2022 - Golem of Prague

An introduction to statistical models, scientific workflow, and DAGs for understanding causal inference.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

UCLA Automated Reasoning Group

Causality - Interventions and Counterfactuals

Dive into causal inference fundamentals, exploring interventions and counterfactuals with intuitive explanations and key results from Bayesian network theory.

• YouTube
• 3 hours 57 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2022 - Counts & Confounds

Explores confounding, sensitivity analysis, proxy variables, Poisson generalized linear models, and innovation-loss modeling.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2022 - Good & Bad Controls

Learn to use DAGs, the backdoor criterion, and control selection to distinguish good and bad controls in causal analysis.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2022 - Elemental Confounds

Learn how forks, pipes, colliders, descendants, and unobserved variables create or obscure confounding in causal diagrams.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Causality: Philosophical Perspectives - Part 1

Explore the concept of causality through the ideas of Aristotle, Hume, Kant, and Mill, gaining insights into philosophical perspectives on cause and effect relationships.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking - Good and Bad Controls - Lecture A07

Discover how to identify and apply appropriate statistical controls while avoiding common pitfalls that can bias your causal inferences and research conclusions.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking - Elemental Confounds I - Lecture A06

Uncover how confounding variables distort causal inference and learn systematic approaches to identify and address these fundamental statistical challenges.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2023 - Good & Bad Controls

Learn how causal implications, do-calculus, and the backdoor criterion distinguish good controls from bad controls in statistical models.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video