causal-learn
Structure_threader
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| causal-learn | Structure_threader | |
|---|---|---|
| 1 | 1 | |
| 982 | 24 | |
| 5.2% | - | |
| 7.9 | 3.3 | |
| 12 days ago | 9 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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causal-learn
Structure_threader
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Two ways to parallelize STRUCTURE
There is also structure_threader which works amazingly for me (Github Link)
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.
directory-structure - :package: Print a directory tree structure in your Python code.
dodiscover - [Experimental] Global causal discovery algorithms
OSGenome - An Open Source Web Application for Genetic Data (SNPs) using 23AndMe and Data Crawling Technologies
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.
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.