PolicyEngine / PolicyEngine/snap-qc-sim

Wire the error model into the simulator

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Python
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Description

Levers become policy configurations passed to the engine; per-case predictions aggregate through the existing sampling layer; keep the v1 accounting bound as a labeled comparison. docs/v2-error-model.md §4.

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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

Read docs/v2-error-model.md §4 first, then trace the simulator's existing sampling layer and how levers reach the engine. The work is complete when levers are passed as policy configurations, per-case predictions aggregate through sampling, and the v1 accounting bound remains as a labeled comparison.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
48/100

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