CausalityTools

CI codecov DOI

CausalityTools.jl is a package for quantifying associations, independence testing and causal inference.

All further information is provided in the documentation, which you can either find online or build locally by running the docs/make.jl file.

Key features

  • Association API: includes measures and their estimators for pairwise, conditional and other forms of association from conventional statistics, from dynamical systems theory, and from information theory: partial correlation, distance correlation, (conditional) mutual information, transfer entropy, convergent cross mapping and a lot more!
  • Independence testing API, which is automatically compatible with every association measure estimator implemented in the package.
  • Causal (network) inference API integrating the association measures and independence testing framework.

Addititional features

Extending on features from ComplexityMeasures.jl, we also offer

  • Discretization API for multiple (multivariate) input datasets.
  • Multivariate counting and probability estimation API.
  • Multivariate information measure API

Installation

To install the package, run import Pkg; Pkg.add("CausalityTools").