cobaya
Code for Bayesian Analysis (by CobayaSampler)
causalnex
A Python library that helps data scientists to infer causation rather than observing correlation. (by mckinsey)
| cobaya | causalnex | |
|---|---|---|
| 1 | 2 | |
| 120 | 2,157 | |
| 6.7% | 1.6% | |
| 8.6 | 5.4 | |
| 26 days ago | 14 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
cobaya
Posts with mentions or reviews of cobaya.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-24.
-
Problem installing Monte Python
Another suggestion would be to consider using a more modern package. Cobaya (https://github.com/CobayaSampler/cobaya) is one such package that would do what you want, but is much more modern and user friendly than MontePython.
causalnex
Posts with mentions or reviews of causalnex.
We have used some of these posts to build our list of alternatives
and similar projects.
-
How many of you still buy and read textbooks after your degree?
I don't claim to defend that this is actually the right way of dealing with those things, but QuantumBlack gave a talk at neurips a couple years back, and really hyped up their package (https://github.com/quantumblacklabs/causalnex) for dealing with this stuff.
-
What are some tools/best practices that Causal Inferencing teams use for experimentation?
As for causal libraries I'd recommend CausalNex, it's the only library that I know that does Judea Pearl's do() operator, and I think that's really great if you want to intervene over causal knowledge (that you'll want).
What are some alternatives?
When comparing cobaya and causalnex you can also consider the following projects:
montepython_public - Public repository for the Monte Python Code
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.
Poetry - Python packaging and dependency management made easy
causalml - Uplift modeling and causal inference with machine learning algorithms