pymc-devs / pymc-devs/pymc-examples
Counterfactual generation using pymc do-operator
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- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
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Description
Notebook proposal
Title: Counterfactual generation using pymc do-operator
Why should this notebook be added to pymc-examples?
This blog explains how to generate counterfactuals using newly introduced do-operator within pymc library. It explains in a very basic way how and why to generate counterfactuals, and how it is a stepping stone towards Causality.
Suggested categories:
- Level: Basic to intermediate
- Diataxis type:
Related notebooks
Cover related notebooks on which this notebook will build,
make sure that the notebook is not duplicated and
explain whether it should be a new notebook or extend an existing one.
References
If applicable, references and material that could help in writing the notebook.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No implementation files or tests are identified. Review existing related notebooks in pymc-examples and the referenced blog, then define a basic-to-intermediate notebook covering counterfactual generation with the PyMC do-operator and its connection to causality. Done means the notebook is added without duplicating an existing example and clearly explains the workflow.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100