causalnex
pgmpy
| causalnex | pgmpy | |
|---|---|---|
| 2 | 2 | |
| 2,144 | 2,617 | |
| 1.0% | 0.7% | |
| 5.4 | 8.0 | |
| 13 days ago | 10 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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causalnex
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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.
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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).
pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
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.
statsmodels - Statsmodels: statistical modeling and econometrics in Python
causalml - Uplift modeling and causal inference with machine learning algorithms
scikit-learn - scikit-learn: machine learning in Python
CausalPy - A Python package for causal inference in quasi-experimental settings
causaldag - Python package for the creation, manipulation, and learning of Causal DAGs
rustworkx - A high performance Python graph library implemented in Rust.
looper - A resource list for causality in statistics, data science and physics
pyhf - pure-Python HistFactory implementation with tensors and autodiff
Keras - Deep Learning for humans
Lottery-Simulation - This program can simulate a number of drawings in the Lottery (6 out of 49). The guesses and the draws are chosen randomly and the user can choose how many right guesses there should be (0-6). Then the program will run through the simulation as many times as it takes to get the exact number of correct guesses the user chose. The user can also choose how many times this should be repeated (the higher the number, the more accurate the result will be). Then the program will automatically calculate the average number of tries it took to get the chosen number of correct guesses and tell the user the chance of getting this certain number of correct guesses.