CAUSAL
INFERENCE ROADMAP

Your guide to learning causal inference

Courses

Master causal inference through structured learning with our curated selection of courses. From beginner-friendly introductions to advanced methods, these courses provide hands-on experience with real datasets and practical applications.

Books

Dive deep into causal inference with essential texts that have shaped the field. These carefully selected books offer both theoretical frameworks and practical guidance, helping you understand everything from basic correlation-causation principles to sophisticated identification strategies.

Articles

Stay current with the latest developments in causal inference through our compilation of influential research papers, blog posts, and case studies. Explore breakthrough methodologies, real-world applications across industries, and expert insights that bridge theory and practice.

Foundations of Causal Inference

Learn the foundations of causal inference, from non-technical introductory books to your first hands-on projects that will give you the confidence needed to further your career

Courses

Causal Inference, by Michael E. Sobel

SMaster the modern science of causal inference with rigorous, hands-on methods to uncover what truly drives cause and effect.

A Crash Course in Causality: Inferring Causal Effects from Observational Data, by Jason A. Roy, Ph.D.

Over five weeks, this hands-on course demystifies “what equals causation,” teaching you to define, visualize, and estimate causal effects in R using modern statistical methods that are transforming research across disciplines.

Essential Causal Inference Techniques for Data Science, A Guided Project by Vinod Bakthavachalam

Discover why A/B testing isn’t enough, master four core causal inference methods in R, and explore cutting-edge techniques that blend causal inference with machine learning.

Free Causal Secrets Mini-Course, by Aleksander Molak

Your Introduction to Causal Thinking With References to Modern Machine Learning and Experimentation

See Course

Communities

Causal Inference Nerds Discord Community

Join 100s of causal inference enthusiasts and gain exclusive access to a journal club, book club, meme channel, and more.

Causal Inference in Statistics Monthly Newsletter

A monthly newsletter packed with resources, community updates, and exclusive causal inference events.

Causal Inference Linkedin
Group

Thousands of professionals exchange content, industry insights, job offerings and more in this vibrant LinkedIn community.

Books

Causal Inference, by Paul R. Rosenbaum

The Book of Why, by Judea Pearl and Dana Mackenzie

Causal Inference in Statistics with Exercises, Practice Projects, and R/Python Code Notebooks, by Justin Belair

Causal Inference: The Mixtape, by Scott Cunningham

Articles

The Influence of Confounding Variables in Observational Studies, by Jesca Birungi

Observational studies play an important role in understanding associations between exposures and outcomes, particularly in fields where randomized controlled trials (RCTs) may not be feasible due to ethical, practical, or financial constraints. However, these studies often face a major challenge; confounding.

The Battle For The Soul Of Causal Inference, by Justin Belair

In causal inference methodology, an intellectual battle of titans has been unfolding for decades. This conflict isn’t merely academic - it represents fundamentally different ways of conceptualizing causality, with major implications for how researchers approach causal questions across disciplines.

Association Does Not Imply Causation, Except When it Does – A Causal Inference Perspective, by Justin Belair

Ever wondered why researchers are so cautious when saying “X causes Y” instead of just “X is associated with Y”? The difference isn’t just semantic—it’s at the heart of scientific rigor and the foundation of evidence-based decision-making.

Potential Outcomes and Graphical Causal Models

At the core, causal inference relies on two major frameworks: the Neyman-Rubin causal model (potential outcomes) and the Structural Causal Models of Judea Pearl, based on graphical causal models

Books

Causal Inference in Statistics: A Primer, by Judea Pearl, Madelyn Glymour, Nicholas P. Jewell

Causal Inference for Statistics, Social, and Biomedical Sciences, An Introduction by Guido W. Imbens and Donald B. Rubin

The Effect: An Introduction to Research Design and Causality, by Nick Huntington-Klein

Conterfactuals and Causal Inference: Methods and Principles for SOcial Research, by Stephen L. Morgan and Christopher Winship

Causal Inference and Discovery in Python, by Aleksander Molak

Articles

Common DAG Structures–Confounding, Collider Bias, and Mediation, by Justin Belair

In this blog post, we’ll explore common DAG structures that frequently appear in causal inference problems, simulate data according to these structures, and demonstrate how different analytical approaches can lead to correct or incorrect causal estimates.

Crash course on confounding, bias, and deconfounding remedies using R, by Andy Wilson & Aimee Harrison

Confounding bias is one of the most ubiquitous challenges in estimating effects from observational (real-world data) studies.

Selection Bias, A Causal Inference Perspective (With Downloadable Code Notebook), by Justin Belair

Collider bias occurs when we condition on (or select based on) a variable that is influenced by both the exposure and outcome of interest.

CAUSAL INFERENCE ROADMAP

Foundations of Causal Inference

Courses

Causal Inference, by Michael E. Sobel

A Crash Course in Causality: Inferring Causal Effects from Observational Data, by Jason A. Roy, Ph.D.

Essential Causal Inference Techniques for Data Science, A Guided Project by Vinod Bakthavachalam

Free Causal Secrets Mini-Course, by Aleksander Molak

Communities

Causal Inference Nerds Discord Community

Causal Inference in Statistics Monthly Newsletter

Causal Inference Linkedin Group

Books

Causal Inference, by Paul R. Rosenbaum

The Book of Why, by Judea Pearl and Dana Mackenzie

Causal Inference in Statistics with Exercises, Practice Projects, and R/Python Code Notebooks, by Justin Belair

Causal Inference: The Mixtape, by Scott Cunningham

Articles

Potential Outcomes and Graphical Causal Models

Books

Causal Inference in Statistics: A Primer, by Judea Pearl, Madelyn Glymour, Nicholas P. Jewell

Causal Inference for Statistics, Social, and Biomedical Sciences, An Introduction by Guido W. Imbens and Donald B. Rubin

The Effect: An Introduction to Research Design and Causality, by Nick Huntington-Klein

Conterfactuals and Causal Inference: Methods and Principles for SOcial Research, by Stephen L. Morgan and Christopher Winship

Causal Inference and Discovery in Python, by Aleksander Molak

Articles

CAUSAL
INFERENCE ROADMAP

Causal Inference Linkedin
Group