dodiscover
causal-learn
| dodiscover | causal-learn | |
|---|---|---|
| 1 | 1 | |
| 59 | 988 | |
| - | 3.4% | |
| 3.0 | 7.9 | |
| 4 days ago | 24 days ago | |
| Python | Python | |
| MIT License | MIT License |
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dodiscover
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Anyone that have worked with Causal discovery
I would check out https://github.com/py-why/dodiscover
causal-learn
What are some alternatives?
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
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.
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
tfcausalimpact - Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
cdci-causality - Python implementation of CDCI, a method to identify causal direction between two variables
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.