CausalPy
pgmpy
CausalPy | pgmpy | |
---|---|---|
2 | 2 | |
805 | 2,624 | |
6.3% | 1.0% | |
9.2 | 8.0 | |
7 days ago | 16 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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CausalPy
- CausalPy: A Python package for causal inference in quasi-experimental settings
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This Week In Python
CausalPy – A Python package for causal inference in quasi-experimental settings
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?
lumi - Lumi is an nano framework to convert your python functions into a REST API without any extra headache.
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
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
mbdpy - Python module for model-based-design
rustworkx - A high performance Python graph library implemented in Rust.
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.
pyhf - pure-Python HistFactory implementation with tensors and autodiff
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
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.