causalnex
looper
| causalnex | looper | |
|---|---|---|
| 2 | 2 | |
| 2,144 | 235 | |
| 1.0% | - | |
| 5.4 | 7.3 | |
| 13 days ago | about 2 months ago | |
| Python | ||
| GNU General Public License v3.0 or later | - |
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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).
looper
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.
Data-science-best-resources - Carefully curated resource links for data science in one place
causalml - Uplift modeling and causal inference with machine learning algorithms
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
scikit-learn - scikit-learn: machine learning in Python
datascience - Curated list of Python resources for data science.
causaldag - Python package for the creation, manipulation, and learning of Causal DAGs
causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Keras - Deep Learning for humans
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.