causal-ai-learning

Notes, exercises, and implementations while studying modern Causal AI.

This repository accompanies my study of two textbooks:

• Causal AI by Robert Osazuwa Ness
• Causal Artificial Intelligence by Elias Bareinboim

The repository contains:

• Notes and summaries
• Exercise solutions
• Code implementations
• Small experiments inspired by the books

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Current Focus

Causal AI (Ness)

Working through selected coding-oriented chapters:

• Chapter 5 — Connecting Causality and Deep Learning
• Chapter 6 — Structural Causal Models
• Chapter 7 — Interventions and Causal Effects
• Chapter 8 — Counterfactuals
• Chapter 9 — General Counterfactual Inference Algorithm
• Chapter 10 — Identification and Causal Hierarchy

Implementations may include:

• Causal VAE
• Neural SCMs
• Counterfactual inference
• Identification examples

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Causal Artificial Intelligence (Bareinboim)

Working through exercises from the chapters most relevant to causal representation learning and structural learning:

• Chapter 2 — Structural Causal Models
• Chapter 4 — Identification of Causal Effects
• Chapter 16 — Structural Learning
• Chapter 17 — Multi-Domain Structural Learning
• Chapter 18 — Causal Representation Learning

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Repository Structure

notes/
├── ness/
└── bareinboim/

exercises/
├── chapter02/
├── chapter04/
├── chapter16/
├── chapter17/
└── chapter18/

implementations/
├── causal_vae/
├── neural_scm/
├── counterfactual_inference/
└── identification/

experiments/

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Goal

Build a solid understanding of:

• Structural Causal Models
• Identification
• Counterfactual Reasoning
• Structural Learning
• Causal Representation Learning

through reading, coding, and solving exercises.