Good material about Causal Inference
- Introduction to Causal Inference: from a Machine Learning Perspective, by Brady Neal
- Causal Inference: What If, by Miguel A. Hernán, James M. Robins
- Causal Inference for The Brave and True, by Matheus Facure Alves
- XAI Stories (Some chapters XAI with Causal Inference)
- Causal Inference for Data Science, by Aleix Ruiz de Villa
- Causal Machine Learning, by Robert Ness
- Elements of Causal Inference, by Jonas Peters, Dominik Janzing, and Bernhard Sch¨olkopf
- How to Move Beyond ML Predictions: An Introduction to Causal Inference
- Applied Causal Analysis (with R), by Paul C. Bauer
- Bayesuvius: a visual dictionary of Bayesian Networks and Causal Inference, by Robert R. Tucci
- The Effect, by Nick Huntington-Klein
- Causality for Machine Learning, by Cloudera
- Causation, Prediction, and Search, by Peter Spirtes, Clark Glymour, and Richard Scheines
- Probabilistic Programming & Bayesian Methods for Hackers (An intro to Bayesian methods and probabilistic programming)
- Introduction to Modern Causal Inference, by Alejandro Schuler and Mark van der Laan
- Econometrics for Business in R and Python, by Diogo Alves de Resende
- Causal Data Science with Directed Acyclic Graphs, by Paul Hunermund
- Causal Inference Course Lectures, by Brady Neal
- A Crash Course in Causality: Inferring Causal Effects from Observational Data, by Jason A. Roy
- Causal Inference, by Michael E. Sobel
- Machine Learning & Causal Inference: A Short Course
- Lecture Notes for Causality in Machine Learning, by Robert Ness
- Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Probabilistic graphical models, by Daphne Coller
- Inferência Causal em Epidemiologia (Portuguese), by Fernando Hartwig
- Regression Modeling for Public Health, by Scott Venners
- Evaluating Impact in Low- and Middle-Income Countries, by Alaka Holla
- Desenhos de Pesquisa para Inferência Causal, UFMG (Portuguese)
- Inferência Causal, PGSC - UFMA (Portguese)
- DoWhy
- Causallib
- CausalNex
- CausalML
- Pylift
- EconML
- CausalLift
- CausalImpact
- Dame-Flame
- Pgmpy
- PyMC
- Causal Inference in Data Science From Prediction to Causation, by Amit Sharma
- Lectures on Causality, by Jonas Peters
- Drawing causal inference from big data – NAS Sackler Colloquium
- Causal inference in everyday machine learning by Ferenc Huszar
- Causal discovery, by Bernhard Scholkopf
- The Effect, by Nick Huntington-Klein
- UCL Seminar - What is Causal AI
- Start Asking Your Data “Why?” - A Gentle Introduction To Causal Inference | PyData Global 2021
- Solutions in Causal Inference
- Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data, by Julia M. Rohrer
- A Survey on Causal Inference, by Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang
- Review of Causal Discovery Methods Based on Graphical Models, by Clark Glymour, Kun Zhang and Peter Spirtes
- Causal inference, by Jane Huang
- Using Causal Inference to Improve the Uber User Experience
- Causal AI — Enabling Data-Driven Decisions
- The Surprising Power of Online Experiments
- Causal Inference and Data Fusion in Econometrics
- Causal Machine Learning and Business Decision Making
- causalscience.org
- Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data, by Karsten Lübke, Matthias Gehrke, Jörg Horst & Gero Szepannek
- Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality, by Thomas S. Richardson & James M. Robins