causal-learn
dodiscover
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| causal-learn | dodiscover | |
|---|---|---|
| 1 | 1 | |
| 978 | 57 | |
| 5.2% | - | |
| 7.7 | 3.8 | |
| 12 days ago | 5 days ago | |
| Python | Python | |
| MIT License | MIT License |
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causal-learn
dodiscover
-
Anyone that have worked with Causal discovery
I would check out https://github.com/py-why/dodiscover
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.
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
tfcausalimpact - Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
sections - Easy Python tree data structures
cdci-causality - Python implementation of CDCI, a method to identify causal direction between two variables
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.