PolicyEngine / PolicyEngine/policyengine-scorecard
External-scores catalog in Ledger (fill in advance of analysis)
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- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- Avg merge
- 6d 12h
- Merged PRs (30d)
- 32
Description
Max: the set of external scores should live 'in something like what ledger is for thesis' — and Ledger already has the typed validation_comparator fact class (distinct from calibration targets; see the EUROMOD comparator ruling). Catalog EVERY external score as comparator facts BEFORE analysis: source package per publisher (Urban SotSN, FNS, ASPE, NTA, Census, KFF, HUD, DWP, HMRC, OBR, JCT, CBO, TPC…), per-value provenance URL + vintage + licence + unit concept. calibration_relationship becomes structural: a fact referenced by a target profile is consumed; comparator-profile-only facts are held_out. First package = Urban SotSN's 30,316 values (already parsed at ~/populace-sotsn-takeup/comparison/urban_tidy.csv). Cataloging is cheap, agent-parallel, and unblocks adapters — start immediately.
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 examining the existing validation_comparator fact class and the parsed source at ~/populace-sotsn-takeup/comparison/urban_tidy.csv. Use Urban SotSN as the first package, then inspect how target profiles reference facts and how comparator-profile-only facts are marked held_out. Done means external values have publisher, provenance URL, vintage, licence, and unit concept recorded before analysis.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- analytics, data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100