pyphi
causal-learn
| pyphi | causal-learn | |
|---|---|---|
| 1 | 1 | |
| 356 | 992 | |
| - | 3.8% | |
| 7.5 | 7.9 | |
| about 2 months ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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pyphi
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Is Conway's Game of Life Conscious According to Integrated Information Theory?
it's not very hard to build a model (and the corresponding transition probability matrix) for a GoL network. and the version 4.0 formalism code is online for anyone to use (https://github.com/wmayner/pyphi). so you could try to answer the question for yourself (though it gets computationally prohibitive for networks bigger than 10 units or so, so...)
causal-learn
What are some alternatives?
dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
NeuroTS - Topological Neuron Synthesis
dodiscover - [Experimental] Global causal discovery algorithms
tfcausalimpact - Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
sections - Easy Python tree data structures
looper - A resource list for causality in statistics, data science and physics
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
Structure_threader - A wrapper program to parallelize and automate runs of "Structure", "fastStructure" and "MavericK".
directory-structure - :package: Print a directory tree structure in your Python code.
genome_integration - MR-link and genome integration. genome_integration is a repository for the analysis of genomic data. Specifically, the repository implements the causal inference method MR-link, as well as other Mendelian randomization methods.