PolicyEngine Review Patterns

Comprehensive patterns for reviewing PolicyEngine implementations.

Understanding WHY, Not Just WHAT

Pattern Analysis Before Review

When reviewing implementations that reference other states:

🔴 CRITICAL: Check WHY Variables Exist

Before approving any state-specific variable, verify:

1. Does it have state-specific logic? - Read the formula
2. Are state parameters used? - Check for parameters(period).gov.states.XX
3. Is there transformation beyond aggregation? - Look for calculations
4. Would removing it break functionality? - Test dependencies

Example Analysis:

# IL TANF has this variable:
class il_tanf_assistance_unit_size(Variable):
    adds = ["il_tanf_payment_eligible_child", "il_tanf_payment_eligible_parent"]
    # ✅ VALID: IL-specific eligibility rules

# But IN TANF shouldn't copy it blindly:
class in_tanf_assistance_unit_size(Variable):
    def formula(spm_unit, period):
        return spm_unit("spm_unit_size", period)
    # ❌ INVALID: No IN-specific logic, just wrapper

Wrapper Variable Detection

Red Flags - Variables that shouldn't exist:

• Formula is just return entity("federal_variable", period)
• Aggregates federal baseline with no transformation
• No state parameters accessed
• Comment says "use federal" but creates variable anyway

Action: Request deletion of unnecessary wrapper variables

Priority Review Checklist

🔴 CRITICAL - Automatic Failures

These issues will cause crashes or incorrect results:

1. Vectorization Violations

❌ FAILS:
if household("income") > 1000:  # Will crash with arrays
    return 500

✅ PASSES:
return where(household("income") > 1000, 500, 100)

2. Hard-Coded Values

❌ FAILS:
benefit = min_(income * 0.33, 500)  # Hard-coded 0.33 and 500

✅ PASSES:
benefit = min_(income * p.rate, p.maximum)

3. Missing Parameter Sources

❌ FAILS:
reference:
  - title: State website
    href: https://state.gov

✅ PASSES:
reference:
  - title: Idaho Admin Code 16.05.03.205(3)
    href: https://adminrules.idaho.gov/rules/current/16/160503.pdf#page=14

🟡 MAJOR - Must Fix

These affect accuracy or maintainability:

4. Test Quality Issues

❌ FAILS:
income: 50000  # No separator

✅ PASSES:
income: 50_000  # Proper formatting

5. Calculation Accuracy

• Order of operations matches regulations
• Deductions applied in correct sequence
• Edge cases handled (negatives, zeros)

6. Description Style

❌ FAILS:
description: The amount of SNAP benefits  # Passive voice

✅ PASSES:
description: SNAP benefits  # Active voice

🟢 MINOR - Should Fix

These improve code quality:

7. Code Organization

• One variable per file
• Proper use of defined_for
• Use of adds for simple sums

8. Documentation

• Clear references to regulation sections
• Changelog entry present

Common Issues Reference

Documentation Issues

Code Issues

Test Issues

Source Verification Process

Step 1: Check Parameter Values

For each parameter file:

✓ Value matches source document
✓ Source is primary (statute > regulation > website)
✓ URL links to exact section with page anchor
✓ Effective dates correct

Step 2: Validate References

Primary sources (preferred):

• USC (United States Code)
• CFR (Code of Federal Regulations)
• State statutes
• State admin codes

Secondary sources (acceptable):

• Official policy manuals
• State plan documents

Not acceptable alone:

• Websites without specific sections
• Summaries or fact sheets
• News articles

Code Quality Checks

Vectorization Scan

See policyengine-vectorization skill for comprehensive vectorization patterns and scan targets.

Hard-Coding Scan

Search for numeric literals — flag anything like:

"0.5"
"100"
"0.33"
"65"

Review Response Templates

For Changes Required

## PolicyEngine Review: CHANGES REQUIRED ❌

### Critical Issues (Must Fix)

1. **Non-vectorized code** - lines 45-50
   ```python
   # Replace this:
   if income > threshold:
       benefit = high_amount

   # With this:
   benefit = where(income > threshold, high_amount, low_amount)

2. Parameter value mismatch - standard_deduction.yaml
   • Source shows $200, parameter has $198
   • Reference: 7 CFR 273.9(d)(1), page 5

Major Issues (Should Fix)

3. Missing primary source - income_limit.yaml
   • Add statute/regulation citation
   • Current website link insufficient

Please address these issues and re-request review.

---

## Test Validation

### Check Test Structure

```yaml
# Verify proper format:
- name: Case 1, description.  # Numbered case with period
  period: 2024-01  # Valid period (2024-01 or 2024)
  input:
    people:
      person1:  # Generic names
        employment_income: 50_000  # Underscores
  output:
    # Calculation documented
    # Income: $50,000/year = $4,167/month
    program_benefit: 250

Run Test Commands

# Unit tests
pytest policyengine_us/tests/policy/baseline/gov/

# Integration tests
policyengine-core test <path> -c policyengine_us

# Microsimulation
pytest policyengine_us/tests/microsimulation/

Review Priorities by Context

New Program Implementation

1. Parameter completeness
2. All documented scenarios tested
3. Eligibility paths covered
4. No hard-coded values

Bug Fixes

1. Root cause addressed
2. No regression potential
3. Tests prevent recurrence
4. Vectorization maintained

Refactoring

1. Functionality preserved
2. Tests still pass
3. Performance maintained
4. Code clarity improved

Large-Scale Refactoring (Renaming)

⚠️ CRITICAL: Variable/function renaming has high potential to break things

When reviewing PRs that rename variables or functions across the codebase:

1. Test Coverage Requirements

   • All existing tests must pass
   • Run microsimulation tests if available
   • Consider running notebooks mentioned in repo docs
   • Check for implicit dependencies (string references, dynamic lookups)

2. Common Breakage Points

   • Variables referenced as strings (e.g., in reforms, API endpoints)
   • Dynamic variable lookups via entity(variable_name, period)
   • Parameter files that reference variable names
   • Documentation/examples that hardcode variable names
   • External tools/APIs that depend on variable naming

3. Validation Strategy

   bashCopy

   # Basic validation
   pytest  # All unit tests
   
   # If notebooks exist, run them
   jupyter nbconvert --execute notebook.ipynb
   
   # Check for string references to renamed variables
   grep -r "old_variable_name" --include="*.py" --include="*.yaml"
   

4. Approval Requirements

   • Even if tests pass, require maintainer review
   • Look for usage in API/web app if variables are exposed
   • Check if any variables are part of the public interface

Quick Review Checklist

Parameters:

• Values match sources
• References include subsections
• All metadata fields present
• Effective dates correct

Variables:

• Properly vectorized (no if-elif-else)
• No hard-coded values
• Uses existing variables
• Includes proper metadata

Tests:

• Proper period format
• Underscore separators
• Calculation comments
• Realistic scenarios

Overall:

• Changelog entry
• Code formatted
• Tests pass
• Documentation complete

Review-Fix Loop Discipline

• The review-fix loop must continue until 0 critical issues are found OR the max round limit is reached. Never stop early when criticals remain, even if other severity levels are clean.
• When an implementation agent creates a correct pattern (e.g., an _in_effect boolean), do not instruct them to remove it in favor of a "simpler" approach that introduces an anti-pattern. Trust domain-specific correctness over superficial simplicity.