dowhy
causal-inference-tutorial
| dowhy | causal-inference-tutorial | |
|---|---|---|
| 8 | 1 | |
| 6,797 | 548 | |
| 2.0% | - | |
| 8.8 | 10.0 | |
| 7 days ago | over 4 years ago | |
| Python | Jupyter Notebook | |
| 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
causal-inference-tutorial
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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.
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
Causality
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.
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.