Benjamin T. Vincent
Benjamin T. Vincent
The existing ANCOVA notebook focusses clearly on a pre-test post-test experimental design. However, the ANCOVA style approach can also be used in situations where we have: - a categorical treatment...
Could be useful to gather user testimonials. See https://saythanks.io Also add to `pyproject.toml`, see example here https://github.com/pypa/sampleproject/blob/aeeb50a948addcb712ad4261df472263514991e1/pyproject.toml#L131-L136
Add type hints and run mypy. - [ ] Fix numpy type hint here https://github.com/pymc-labs/CausalPy/blob/d74a5a8d60c66c516c7f0a713ec80b0a730012de/causalpy/plot_utils.py#L11
Suggestion by @juanitorduz # Resources * https://github.com/MasaAsami/pysynthdid * https://matheusfacure.github.io/python-causality-handbook/25-Synthetic-Diff-in-Diff.html
After #22, we need to think if we need a bit of an API change. At the moment we just specify a model, model formula, and data. When we create...
At the moment we have reasonable test coverage, and that's mostly coming from the integration tests. But those tests just check that no errors happen, not whether the results are...
At the moment, all the examples show very clear causal impacts. But it would be nice to add an example without any causal impact, particularly if it demonstrates how one...
Suggestion by @juanitorduz... Rather than just applying the package to synthetic datasets, it would be good to apply the methods to classic datasets / causal inference problems. This also gives...
- [ ] Add more model variants (ie interactions) to reflect those in Reichardt. - [ ] Update image example in README. - [ ] add more explanatory text -...
**We are not removing custom PyMC models.** It makes a lot of sense to be able to write custom PyMC models, for maximum flexibility. But for the majority of cases,...