pymc-labs / pymc-labs/CausalPy
SC: check robustness of results (frequentist)
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- Dominant language
- Python
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- Merged PRs (30d)
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Description
I've experienced clearly sub-optimal weightings when running the the WeightedProportion custom scikit-learn model. It is likely due to bad optimisation, perhaps getting stuck by local optima. So we need to explore the dependence of the results upon w_start.
One way to approach making the results more reliable (more likely to represent the global minimum) is to use a particle swarm type approach where we run the optimisation multiple times, each with different w_start.
- Look into the relevant fitting procedures in scikit-learn.
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
Start by reading causalpy/skl_models.py around lines 22–33 and inspect the relevant scikit-learn fitting procedures. Investigate how results vary with w_start and whether repeated optimization improves reliability; document the findings and the resulting robustness direction.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100