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https://github.com/kailashbuki/caddie
Information-Theoretic Causal Inference on Discrete Data
https://github.com/kailashbuki/caddie
Last synced: 12 days ago
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Information-Theoretic Causal Inference on Discrete Data
- Host: GitHub
- URL: https://github.com/kailashbuki/caddie
- Owner: kailashbuki
- License: mit
- Created: 2019-09-23T09:53:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-27T15:36:29.000Z (almost 2 years ago)
- Last Synced: 2024-11-28T23:40:59.318Z (26 days ago)
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Caddie
-------
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`:
```console
$ pip install caddie
```
Caddie officially supports Python >= 3.6.
How to use caddie?
------------------
```pycon
>>> 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.