Table of Contents

• Books

  • The Book of Why
  • Causal Inference in Statistics: A Primer
  • Counterfactuals and Causal Inference: Methods and Principles for Social Research
  • Causality: Models, Reasoning and Inference
  • Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
  • Causal Inference: The Mixtape
  • Causal Inference for Data Science
  • The Book of Why
  • Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
  • Causal Inference in Statistics: A Primer
  • Causal Inference Book

• Courses

  • Introduction to Causal Inference (Fall2020)
  • A Crash Course in Causality: Inferring Causal Effects from Observational Data
  • Causal ML Mini Course
  • Causal Inference with R - Introduction

• Videos and Lectures

  • Lectures on Causality: 4 Parts
  • Towards Causal Reinforcement Learning (CRL) - ICML'20 - Part I
  • Towards Causal Reinforcement Learning (CRL) - ICML'20 - Part II
  • On the Causal Foundations of AI
  • Judea Pearl: Causal Reasoning, Counterfactuals, and the Path to AGI | Lex Fridman Podcast #56
  • NeurIPS 2018 Workshop on Causal Learning
  • Causal Inference Bootcamp
  • NeurIPS 2018 Workshop on Causal Learning

• Tools

  • Ananke: A module for causal inference (Python)
  • Causal ML: A Package for Uplift Modeling and Causal Inference with ML (Python)
  • CausalNex: A toolkit for causal reasoning with Bayesian Networks (Python)
  • pgmpy: Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks
  • CausalNex: A toolkit for causal reasoning with Bayesian Networks (Python)