Welcome to CausalNex’s API docs and tutorials!

Introduction

• Introduction
  • Main features of CausalNex
  • Learning About CausalNex
  • Assumptions

Getting Started

• Installation prerequisites
  • macOS / Linux
  • Windows
  • Python virtual environments
• Installation guide

Tutorial

• A first CausalNex tutorial
  • Structure Learning
  • Fitting the Conditional Distribution of the Bayesian Network
  • Model Probability
  • Model Quality
  • Querying Marginals
  • Do Calculus

User guide

• Causal Inference with Bayesian Networks. Main Concepts and Methods
  • 1. Causality
  • 2. Bayesian Networks (BNs)
  • 3. BayesianNetwork
  • 4. Querying model and making interventions with InferenceEngine

Resources

• Frequently asked questions
  • What is CausalNex?
  • What are the benefits of using CausalNex?
  • When should you consider using CausalNex?
  • Why NOTEARS algorithm over other structure learning methods?
  • What is the recommended type of dataset to be used in NOTEARS?
  • What is the recommended number of samples for satisfactory performance?
  • Why can my StructureModel be cyclic, but not my BayesianNetwork?
  • Why a separate data pre-processing process for probability fitting than structure learning? / Why discretise data in probability fitting?
  • Why call fit_node_states before fit_cpds?
  • What is Do-intervention and when to use it?
  • How can I make inference faster?
  • How does CausalNex compare to other projects, e.g. CausalML, DoWhy?
  • What version of Python does CausalNex use?
  • How do I upgrade CausalNex?
  • How can I find out more CausalNex?
  • Where can I learn more about Bayesian Networks?

API Docs

causalnex

causalnex toolkit for causal reasoning (Bayesian Networks / Inference)

Indices and tables

• Index
• Module Index
• Search Page