causalnex
0.4.2

Introduction

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

Getting Started

  • Installation prerequisites
    • macOS / Linux
    • Windows
    • Python virtual environments
      • Using conda
        • Create an environment with conda
        • Activate an environment with conda
        • Other conda commands
      • Alternatives to conda
  • Installation guide

Tutorial

  • A first CausalNex tutorial
    • Structure Learning
      • Structure from Domain Knowledge
      • Visualising the Structure
      • Learning the Structure
      • Preparing the Data for Structure Learning
      • Modifying the Structure
    • Fitting the Conditional Distribution of the Bayesian Network
      • Preparing the Discretised Data
      • Cardinality of Categorical Features
      • Discretising Numeric Features
      • Create Labels for Numeric Features
      • Train / Test Split
    • Model Probability
      • Fit Conditional Probability Distributions
      • Predict the State given the Input Data
    • Model Quality
      • Classification Report
      • ROC / AUC
    • Querying Marginals
      • Baseline Marginals
      • Marginals after Observations
    • Do Calculus
      • Updating a Node Distribution
      • Resetting a Node Distribution
      • Effect of Do on Marginals

User guide

  • Causal Inference with Bayesian Networks. Main Concepts and Methods
    • 1. Causality
      • 1.1 Why is causality important?
      • 1.2 Structural Causal Models (SCMs)
    • 2. Bayesian Networks (BNs)
      • 2.1 Directed Acyclic Graph (DAG)
      • 2.2 What Bayesian Networks are and are not
      • 2.3 Advantages and Drawbacks of Bayesian Networks
    • 3. BayesianNetwork
      • 3.1 Defining the DAG with StructureModel
      • 3.2 Likelihood Estimation and Predictions with BayesianNetwork
    • 4. Querying model and making interventions with InferenceEngine
      • 4.1 Querying marginals with InferenceEngine.query
      • 4.2 Making interventions (Do-calculus) with InferenceEngine.do_intervention

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.structure
      • causalnex.structure.notears
        • causalnex.structure.notears.from_numpy
        • causalnex.structure.notears.from_numpy_lasso
        • causalnex.structure.notears.from_pandas
        • causalnex.structure.notears.from_pandas_lasso
      • causalnex.structure.StructureModel
    • causalnex.plots
      • causalnex.plots.plot_structure
    • causalnex.discretiser
      • causalnex.discretiser.Discretiser
    • causalnex.network
      • causalnex.network.BayesianNetwork
    • causalnex.evaluation
      • causalnex.evaluation.classification_report
      • causalnex.evaluation.roc_auc
    • causalnex.inference
      • causalnex.inference.InferenceEngine
causalnex
  • Docs »
  • Python Module Index

Python Module Index

c
 
c
- causalnex
    causalnex.discretiser
    causalnex.evaluation
    causalnex.inference
    causalnex.network
    causalnex.plots
    causalnex.structure
    causalnex.structure.notears

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