• Videos
• Books
• Forums
• Courses
• Academic papers
• Conferences and Courses
• Websites
• Software tools

Where can I learn more about Causality and Causal Inference?

You can find out about many of the core concepts and ideas in our Help articles. They also explain some maths terminology we can’t avoid using, and have lots of links back to relevant content elsewhere on the Internet. We try to make Causal Wizard easy to use, but we don’t want to hide important words you might need later, if you want to discuss your results with a statistician or data scientist.

You don’t need to read anything to start using Causal Wizard, but if you want to learn more about how it works and how to interpret your results, we recommend the following websites, videos and articles:

Videos

For those wanting to be convinced about the importance of explicitly grappling with Causality:

• https://www.youtube.com/watch?v=KNPYUVmY3NM Science Before Statistics: Causal Inference, by Richard McElreath.
• https://www.youtube.com/watch?v=Ts0hnNBRIWg Introduction To Causal Inference And Directed Acyclic Graphs , by Peter WG Tennant in a workshop for the UK Reproducibility Network (UKRN).
• https://www.youtube.com/watch?v=DXBPtpBhGqo A Brief Introduction to Causal Inference (Course Preview), by Brady Neal.

Books

If you're not sure which book to read, check out this handy flowchart which helps you decide!

• https://theeffectbook.net/ The Effect: An Introduction to Research Design and Causality, by Nick Huntington-Klein.
• https://matheusfacure.github.io/python-causality-handbook Causal Inference for The Brave and True, by Matheus Facure Alves Alves.
• https://en.wikipedia.org/wiki/The_Book_of_Why The book of Why - history of the Do-Calculus and Pearlian approach to Causality. Authors Judea Pearl and Dana Mackenzie. E-book can be purchased online.
• https://xcelab.net/rm/statistical-rethinking/ Statistical Rethinking , by Richard McElreath. Note this is a course (wait list form available here) online lectures, a Git repository AND a book!
• https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference: What If (the book), by Miguel Hernan.
• https://alexdeng.github.io/causal/index.html Causal Inference and its applications in Online Industry. Written by Alex Deng
• https://mixtape.scunning.com/ Causal Inference The Mixtape. Deep dive slides and articles about various topics in Causal Inference, by Scott Cunningham.
• https://www.amazon.com/Causal-Inference-Discovery-Python-learning/dp/1804612987 Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more. Written by Aleksander Molak.
• https://web.stanford.edu/~swager/causal_inf_book.pdf Causal Inference: A Statistical Learning Approach - a free PDF textbook by Stanford's Stefan Wager, connecting classical causal inference to modern statistical/machine learning methods.

Forums

• https://www.reddit.com/r/CausalInference/ CausalInference subreddit (Reddit community) if you want to read or ask about Causal Inference
• https://www.matej-zecevic.de/cdg/ Causality Discussion Group (CDG) - a community with regular Zoom meets, Slack, and a mailing list.
• https://stats.stackexchange.com/ Stats.StackExchange - a forum to search for questions and answers about Causal Inference, or ask your own question!

Courses

• https://www.bradyneal.com/causal-inference-course Brady Neal's Causal Inference course - a great intro to the topic. See also the video lectures on YouTube. Highly recommended for beginners with some background in statistics.
• https://causalml-book.org/ Applied Causal Inference Powered by ML and AI - a free online course/textbook by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler and Vasilis Syrgkanis (the team behind Double ML, one of the estimators Causal Wizard offers). Every chapter has a companion lab you can run directly in Google Colab - the same free, no-install workflow Causal Wizard's own notebooks use.
• https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus Statistical Rethinking online lectures by Richard McElreath
• https://arxiv.org/abs/2305.18793 A first course in Causal Inference by Peng Ding. We weren't sure whether this is a book, but it takes you on a detailed, gentle journey and it intends to be a course, so here it is!
• https://www.youtube.com/watch?v=Z0ZcsxI-HTs&list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G- Machine Learning and Economics: An Introduction by Susan Athey, Stanford Graduate School of Business.
• https://causalab.sph.harvard.edu/courses/ CAUSALab's annual Causal Inference Courses - for scientists, prerequisites apply.

Academic papers

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081045/ Methods for Evaluating Causality in Observational Studies (2020)
• https://www.nber.org/system/files/working_papers/w26104/w26104.pdf Paper comparing the Potential Outcome and Causal Diagram approaches to Causality (2019).
• https://arxiv.org/abs/1206.6831 "Pearl's Calculus of Intervention Is Complete." Key paper showing that the do-calculus is both sufficient and complete. (2012)
• https://arxiv.org/abs/2011.04216 "DoWhy: An End-to-End Library for Causal Inference". A paper about DoWhy, the most important backend library used in Causal Wizard (2020).
• https://arxiv.org/abs/2206.15475 Causal Machine Learning: A Survey and Open Problems (2022)
• https://journals.sagepub.com/doi/full/10.1177/2515245917745629 Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2018)
• https://link.springer.com/article/10.1007/s11098-009-9450-2 What are randomised controlled trials good for? (2010)
• https://arxiv.org/abs/2405.08793 A Brief Introduction to Causal Inference in Machine Learning - concise lecture notes covering the potential outcomes and structural causal model frameworks together (2024)
• https://arxiv.org/abs/2403.02467 Applied Causal Inference Powered by ML and AI - the paper version of the free textbook listed under Courses, introducing Double ML and related debiased estimation methods (2024)

Conferences

• https://www.causalscience.org/ Causal Data Science Meeting: annual conference series about Causal Inference and Causality in Data Science, held online - talks from past years are on their YouTube channel.
• https://www.continualcausality.org/ Continual Causality: recurring workshop/bridge program (most recently at AAAI) about Causal Inference and Continual Machine Learning.
• https://www.cclear.cc/ CLeaR (Causal Learning and Reasoning): annual academic conference on causal discovery, causal inference and machine learning.
• https://sci-info.org/ Society for Causal Inference (SCI): a professional society with an annual meeting aimed at applied researchers (epidemiology, statistics, data science).

Websites

• https://github.com/matteocourthoud/awesome-causal-inference An actively-maintained, extensive curated list of causal inference courses, books, libraries, papers and industry applications - a great single jumping-off point if this whole page still isn't enough for you.
• https://causality.cs.ucla.edu/blog/ Causal Analysis in Theory and Practice - Judea Pearl's own blog, discussing causal inference concepts, papers and controversies directly from one of the field's founders.
• https://github.com/jvpoulos/causal-ml A curated list of academic resources about Causality.
• http://shubhanshu.com/awesome-causality/ Collection of Open-Source resources about Causality, and Causal Inference, including examples.
• https://causalpython.io/ Mailing list and excellently presented introductory topics in causality.
• https://towardsdatascience.com/causal-inference-what-why-and-how-d7336b0081ec A quick introduction to Causal Inference
• https://www.andrewheiss.com/blog/2021/09/07/do-calculus-backdoors/ Exploring the three rules of do-calculus in plain language

Software tools

• https://www.pywhy.org/ Collection of Open-Source software tools for Causal Machine Learning, including DoWhy (see below) and EconML.
• https://www.pywhy.org/dowhy/v0.9.1/ Documentation for DoWhy, part of PyWhy. DoWhy is used by Causal Wizard
• http://www.dagitty.net/dags.html DAGitty, a Directed Acyclic Graph (DAG) editor which has some of the same features as Causal Wizard.
• https://github.com/matteocourthoud/awesome-causal-inference/blob/main/src/libraries.md A maintained, up-to-date list of Python and R causal inference libraries - a better long-term reference than any single snapshot of "the ecosystem" (like an old social-media thread) can be.
• https://github.com/mckinsey/causalnex CausalNex (archived, no longer maintained as of mid-2026) - a Python library for modelling and inference via Bayesian Networks, from either data or your own domain knowledge in the form of a Directed Acyclic Graph (DAG). Still useful to read even though it won't receive further updates. Documentation here: https://causalnex.readthedocs.io/en/latest/.