dowhy
Causality
| dowhy | Causality | |
|---|---|---|
| 8 | 4 | |
| 6,781 | 449 | |
| 1.7% | 0.0% | |
| 8.8 | 0.0 | |
| 2 days ago | about 3 years ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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dowhy
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Causality for Machine Learning
I'm a fan of the Do Why library out of Microsoft. Even as a novice in the field of causal modeling it can get you up and running by estimating the causal graph based on your data. https://github.com/py-why/dowhy
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Acceptable data formats for Predictive Stepwise Logistic Regression
considering how well understood the generating process is, causal analysis could potentially be very powerful here and would model the "not every possible combination of variables is represented" component extremely naturally. https://github.com/py-why/dowhy
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Do you use any specific framework when it comes to causal inference?
The Do-why package could be useful.
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Causal Explanations Considered Harmful: On the logical fallacy of causal projection
Here's one from Microsoft! https://github.com/py-why/dowhy
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[Q] What are some of the most useful topics/classes in philosophy for Statistics?
Before those discussions, it's good to understand the very basics of the topic so you 1) demonstrate momentum to the prof, and 2) have the basis for a meaningful discussion. For causal reasoning, you can check out the Pearl book Causal inference in statistics, a primer, which is short and readable. Definitely check out the Do Why python package which has good tutorials and videos.
- [R] DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
- DoWhy is a Python library for causal inference
Causality
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Simpson's Paradox
It’s actually surprisingly common. You can even find it in “classical” toy datasets like Iris: https://github.com/DataForScience/Causality/blob/master/1.2%...
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Causality for Machine Learning
I also have a series of blog posts on the topic: https://github.com/DataForScience/Causality where I work through Pearls Primer: https://amzn.to/3gsFlkO
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Review: The Book of Why
You might enjoy my blog series on Causality where I work through Pearls 'Causal Inference in Statistics: A Primer' using Python:
https://github.com/DataForScience/Causality
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Graph Algorithms for Data Science
True, there isn’t much there yet. It was just launched today, after all.
If you want to check out something a bit more substantive, how about this: https://github.com/DataForScience/Causality
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
looper - A resource list for causality in statistics, data science and physics
causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
causalgraph - A python package for modeling, persisting and visualizing causal graphs embedded in knowledge graphs.
CausalPy - A Python package for causal inference in quasi-experimental settings
pyphi - A toolbox for integrated information theory.
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
causal-inference-tutorial - Repository with code and slides for a tutorial on causal inference.
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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.