PolicyEngine / PolicyEngine/microcosm

Drop raw-source and build-intermediate columns from the published populace-us artifact

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Dominant language
Python
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4
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1d 3h
Merged PRs (30d)
94

Description

Summary

The published populace-us artifact should store PolicyEngine-recognized inputs plus explicit structural/geo columns, not raw source-survey fields or build scratch columns.

Drop (raw source + build intermediates)

  • Raw CPS ASEC columns: A_AGE, A_FTPT, WSAL_VAL, SEMP_VAL, WC_VAL, DIS_VAL1, etc.
  • Raw SCF columns such as scf_net_worth, scf_bank_account_assets, and other scf_* source fields once the PE inputs they feed are set.
  • Build scratch: _half, _orig_household_id, *_is_puf_clone, new_tax_unit_id, and redundant intermediate weights if they duplicate calibrated weights.

Keep

  • Geo identifiers used by the local-area pipeline: block_geoid, tract_geoid, congressional_district_geoid, cbsa, etc.
  • Structural keys and calibrated weights.

Acceptance criteria

  • The export surface has an explicit structural/geo allow-list.
  • Raw source and scratch columns are removed upstream before final artifact writing.
  • A gate fails when an artifact contains columns outside PE variables plus the allow-list.
  • Tests cover a raw-source column, a scratch column, and a retained geo/structural column.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Locate the populace-us export and the upstream final-artifact writing path, then trace where raw CPS/SCF fields and build scratch columns enter the pipeline. Add the explicit structural/geo allow-list and artifact gate, and run the relevant export tests to verify raw-source and scratch columns are rejected while a retained geo or structural column remains.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data-engineering
Issue type
Refactor
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
52/100

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