Causal Inference

• Replacing the do-calculus with Bayes rule
• Causal inference with Bayes rule
• The Seven Tools of Causal Inference with Reflections on Machine Learning

See documents tagged withcausality

Resources 📚

• Introduction to Causal Inference by Brady Neal
• A Primer on Causal Analysis
• Causal Inference: What If by Miguel A. Hernán, James M. Robins
• Causality101
• If correlation doesn’t imply causation, then what does? – DDI
• inference by Ferenc Huszár:
  • Part 1: Intro to causal inference and do-calculus
  • Part 2: Illustrating Interventions with a Toy Example
  • Part 3: Counterfactuals
  • Part 4: Causal Diagrams, Markov Factorization, Structural Equation Models
    • clipped: Causal inference 4 Causal Diagrams, Markov Factorization, Structural Equation Models
• Causal Inference With Python Part 2 - Causal Graphical Models
• Causal Inference with Bayesian Networks. Main Concepts and Methods from the CausalNex docs
• Stanford Encyclopedia of Philosophy
  • Causal Models (Stanford Encyclopedia of Philosophy)
  • Probabilistic Causation (Stanford Encyclopedia of Philosophy)
  • Counterfactual Theories of Causation (Stanford Encyclopedia of Philosophy)
  • Causation and Manipulability (Stanford Encyclopedia of Philosophy)

See also

• Structural equation modeling - Wikipedia

Implementation & Tools

• DoWhy | An end-to-end library for causal inference
  • paper: DoWhy An End-to-End Library for Causal Inference
• ijmbarr/causalgraphicalmodels Causal Graphical Models in Python
• Causal Graphical Models and the package (pip install cgm) - A python library for building causal graphical models, closely following Daphne Koller’s Coursera course on Probabilistic Graphical Models, and her 2009 book Probabilistic Graphical Models: Principles and Techniques