auton-survival
causalnex
| auton-survival | causalnex | |
|---|---|---|
| 1 | 2 | |
| 296 | 2,147 | |
| 2.0% | 1.2% | |
| 4.4 | 5.4 | |
| about 1 month ago | 18 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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auton-survival
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).
What are some alternatives?
keras - Deep Learning for humans [Moved to: https://github.com/keras-team/keras]
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.
xgboost-survival-embeddings - Improving XGBoost survival analysis with embeddings and debiased estimators
causalml - Uplift modeling and causal inference with machine learning algorithms
pycox - Survival analysis with PyTorch
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
scikit-learn - scikit-learn: machine learning in Python
autoprognosis - A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
causaldag - Python package for the creation, manipulation, and learning of Causal DAGs
looper - A resource list for causality in statistics, data science and physics
Keras - Deep Learning for humans