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CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional independence testing.
Features
- A dedicated API for causal network inference based on these measures and independence tests
- Causal inference, and quantification of association in general
- Documentation available
- Examples available
- Univariate timeseries
- Categorical data can be used with ContingencyMatrix to compute various information theoretic measures
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Data VisualizationLicense
MIT LicenseFollow CausalityTools.jl
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