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Information-Theoretic Causal Inference on Discrete Data

Project description


Caddie is a collection of bivariate discrete causal inference methods based on information-theoretic Additive Noise Models (ANM) and MDL-based instantiation of Algorithmic Independence of Conditionals (AIC).

Caddie Module Installation

The recommended way to install the caddie module is to simply use pip:

$ pip install caddie

Caddie officially supports Python >= 3.6.

How to use caddie?

>>> X = [1] * 1000
>>> Y = [-1] * 1000
>>> from caddie import cisc
>>> cisc.cisc(X, Y)                                                   # CISC
(0.0, 0.0)
>>> from caddie import anm, measures
>>> anm.fit_both_dir(X, Y, measures.StochasticComplexity)             # CRISP
(0.0, 0.0)
>>> anm.fit_both_dir(X, Y, measures.ChiSquaredTest)                   # DR
(1.0, 1.0)
>>> anm.fit_both_dir(X, Y, measures.ShannonEntropy)                   # ACID
(0.0, 0.0)
>>> from caddie import simulations
>>> simulations.simulate_decision_rate_against_data_type('/results/dir/') # for decision rate vs data type plots
>>> simulations.simulate_accuracy_against_sample_size('/results/dir/')    # for accuracy/decidability vs sample size plots

How to cite the paper?

Todo: Add the citation to thesis.

Project details

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