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Naive, constrained resampling

If you need to truncate the furnishing distributions of your uncertain datasets before applying a causality test, use the following method.

Note

Uncertain data handling relies on the UncertainData. To use the resampling schemes, you need to load this package by first running using UncertainData in the Julia console.

ConstrainedTest

# CausalityTools.CausalityTests.ConstrainedTest — Type.

ConstrainedTest(test::CausalityTest, constraints::ConstrainedResampling, n::Int)
ConstrainedTest(test::CausalityTest, constraints::ConstrainedResampling)

A causality test where the supports of the uncertain values furnishing the uncertain values in the datasets are truncated (according to the provided constraints) prior to performing the test.

n controls the number of independent resamplings over which the test is performed (if not provided, n is set to 1 by default).

Examples

Assume we want to apply a causality test to two uncertain datasets X and Y, but restricting the ranges of values their elements can take while resampling.

# Constraints for X.indices and X.values
cx = (TruncateQuantiles(0.3, 0.7), TruncateStd(1.5))

# Constraints for Y.indices and Y.values
cy = (TruncateQuantiles(0.3, 0.7), TruncateStd(1.5))

# Need to gather to feed to the ConstrainedTest constructor.
cs = ConstrainedIndexValueResampling(cx, cy)

# A cross mapping test applied to 100 independent realisations
# of `X` and `Y`, resampled after constraining the data.
ctest = ConstrainedTest(CrossMappingTest(), cs, 100)

source

Call signature

# CausalityToolsBase.causality — Method.

causality(source::AbstractUncertainIndexValueDataset, 
    target::AbstractUncertainIndexValueDataset, 
    test::ConstrainedTest{CT, CR}) where {CT, CR}

Apply a causality test of type CT to test.n independent realisations of source and target, after first constraining the supports of the uncertain values furnishing the datasets.