Causality

This repository provides implemented algorithms for causal inference between two variables and learning causal structures in graphs. All code is written in Python 3, using SciPy, NumPy, Scikit-learn, Hyppo, and NetworkX. Jupyter notebooks are provided to show analyses.

Note: These are implemented algorithms and study for assignments of Causal Inference taught by Prof. Salehkalyebar in Fall 2020. This repository is in its early phases.

Table of Contents

• Causal Inference Between Two Variables
• Learning Causal Skeleton in Multivariate Setting
• Calculating Size of the Markov Equivalence Class

Environment Requirement

• Python 3.6.5
  • NumPy 1.14.3
  • SciPy 1.5.4
  • Scikit-learn 0.23.2
  • Hyppo 0.1.3
  • NetworkX 2.5

Resources

Class:

• Causal Inference (Sharif, Fall 2020)

Textbooks:

• Elements of Causal Inference: Foundations and Learning Algorithms (2018)
• Causality: Models, Reasoning and Inference (2009)

Paper:

• A linear non-gaussian acyclic model for causal discovery (2006)