pymc-labs / pymc-labs/CausalPy
ATE, CATE, ATT, ATC
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- Python
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Description
This issue will likely be touched by a number of other issues as we flesh out the quantitative outputs and work through more examples. But it is important to go beyond the slightly vague 'causal impact' terminology to be more specific about:
- Average Treatment Effect (ATE)
- Conditional Average Treatment Effect (CATE)
- Average Treatment Effect on the Treated (ATT)
- Average Treatment Effect on the Control (ATC)
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 reviewing the existing quantitative outputs and the examples referenced by this issue. Define how the project will distinguish ATE, CATE, ATT, and ATC, then verify that the relevant outputs and examples use those specific terms instead of the vague “causal impact” label.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100