The Effect
Nick Huntington-Klein's approachable introduction to research design and causality with observational data.
Learning Resources
Books, courses, and practical tools for learning causal inference.
Books
From intuition to identification and estimation.
Causal Inference: What If
Miguel Hernán and James Robins' rigorous treatment of potential outcomes, with code and data.
Causal Inference: The Mixtape
Scott Cunningham's applied guide to design-based methods, with examples in R and Stata.
A First Course in Causal Inference
Peng Ding's statistics-first Berkeley lecture notes, including exercises and formal derivations.
Online courses
Lectures, notes, examples, and exercises.
Introduction to Causal Inference
Brady Neal's complete first course with videos, slides, readings, and an evolving textbook.
Methods for Causal Inference
The University of Edinburgh's open course materials on effect estimation, causal graphs, discovery, and Python exercises.
Causal and Statistical Reasoning
Carnegie Mellon University's open course with simulations, case studies, comprehension checks, and computer tutors.
Machine Learning and Causal Inference
Stanford's short video course on treatment effects, heterogeneity, observational studies, and policy learning.
Practical resources
Tools for diagrams, estimation, and discovery.
DAGitty tutorials and diagram tool
Interactive exercises on variable roles, d-separation, Simpson's paradox, and covariate adjustment.
Causal Inference for the Brave and True
An open, practical handbook for impact estimation with reproducible Python notebooks.
DoWhy
A Python library structured around modelling, identifying, estimating, and refuting causal effects.
ggdag
An R package for creating and analysing causal diagrams with the grammar of graphics.
EconML
Machine-learning methods for estimating heterogeneous treatment effects from experimental or observational data.
causal-learn
A Python toolkit for classical and recent causal discovery algorithms.