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Causality from time series

The causality function and its methods provide a common interface for testing causal hypotheses. For analysing time series, all you need to do is provide a source and a target. Then, choose one of the available causality tests to quantify the (directional) dynamical dependence between source and target.

For data with uncertainties, see uncertainty handling.

# CausalityToolsBase.causality — Method.

causality(source::AbstractVector, AbstractVector, test::CausalityTest)

Test for a causal influence from source to target using the provided causality test.

Examples

x, y = rand(300), rand(300)

# Define some causality tests and apply them to `x` and `y`.
test_ccm = ConvergentCrossMappingTest(timeseries_lengths = [45, 50], n_reps = 20)
test_cm = CrossMappingTest(n_reps = 10)
test_vf = VisitationFrequencyTest(binning = RectangularBinning(5), ηs = 1:5)
test_tog = TransferOperatorGridTest(binning = RectangularBinning(5), ηs = 1:5)
test_jdd = JointDistanceDistributionTest()
test_jddt = JointDistanceDistributionTTest()
predtest = VisitationFrequencyTest(binning = RectangularBinning(5), ηs = -5:5)
test_pa = PredictiveAsymmetryTest(predictive_test = predtest)

causality(x, y, test_ccm)
causality(x, y, test_cm)
causality(x, y, test_vf)
causality(x, y, test_tog)
causality(x, y, test_jdd)
causality(x, y, test_jddt)
causality(x, y, test_pa)

source