dodiscover
[Experimental] Global causal discovery algorithms (by py-why)
cdci-causality
Python implementation of CDCI, a method to identify causal direction between two variables (by soelmicheletti)
| dodiscover | cdci-causality | |
|---|---|---|
| 1 | 2 | |
| 59 | 3 | |
| - | - | |
| 3.0 | 10.0 | |
| 4 days ago | almost 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dodiscover
Posts with mentions or reviews of dodiscover.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Anyone that have worked with Causal discovery
I would check out https://github.com/py-why/dodiscover
cdci-causality
Posts with mentions or reviews of cdci-causality.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing dodiscover and cdci-causality you can also consider the following projects:
causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.