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Calibration matches record counts rather than population margins · case 01

Calibration matches record counts rather than population margins.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Each poststratum is assigned equal respondent weights ignoring base weights.

VERIFIED REPAIR

Multiply each base weight by known cell population divided by the sum of base weights in that cell.

Unsuccessful approach: Global calibration achieves only the grand total and misses cell margins.

Case contract

Rows are (cell,positive base weight); margins give positive population totals for exactly the represented cells. Return weights in input order rounded to eight decimals; empty inputs return empty list.

Why this case matters

A deterministic survey-design model isolates this sampling contract before it is embedded in a larger estimation pipeline.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(rows, margins):
    return [round(margins[c]/sum(k==c for k,v in rows),8) for c,w in rows]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('unequal base weights',solve([('a',N),('a',3*N)],{'a':8*N}),[2*N,6*N])
check('separate margins',solve([('a',N),('b',N)],{'a':2*N,'b':6*N}),[2*N,6*N])
check('already calibrated',solve([('a',N),('a',3*N)],{'a':4*N}),[N,3*N])
check('single observation',solve([('a',N)],{'a':7*N}),[7*N])
check('row order preserved',solve([('b',N),('a',N),('b',3*N)],{'a':2*N,'b':8*N}),[2*N,2*N,6*N])
check('empty cells',solve([],{}),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
unequal base weights[4.0, 4.0][2, 6]Failed
separate margins[2.0, 6.0][2, 6]Passed
already calibrated[2.0, 2.0][1, 3]Failed
single observation[7.0][7]Passed
row order preserved[4.0, 2.0, 4.0][2, 2, 6]Failed
empty cells[][]Passed

SHA-256 / ba3f423e108b7523dad10abcfa0475dcf9e24ef8322f49b3ff12c4ebabe29385

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(rows, margins):
    total=sum(margins.values()); base=sum(w for c,w in rows)
    return [round(w*total/base,8) for c,w in rows]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('unequal base weights',solve([('a',N),('a',3*N)],{'a':8*N}),[2*N,6*N])
check('separate margins',solve([('a',N),('b',N)],{'a':2*N,'b':6*N}),[2*N,6*N])
check('already calibrated',solve([('a',N),('a',3*N)],{'a':4*N}),[N,3*N])
check('single observation',solve([('a',N)],{'a':7*N}),[7*N])
check('row order preserved',solve([('b',N),('a',N),('b',3*N)],{'a':2*N,'b':8*N}),[2*N,2*N,6*N])
check('empty cells',solve([],{}),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
unequal base weights[2.0, 6.0][2, 6]Passed
separate margins[4.0, 4.0][2, 6]Failed
already calibrated[1.0, 3.0][1, 3]Passed
single observation[7.0][7]Passed
row order preserved[2.0, 2.0, 6.0][2, 2, 6]Passed
empty cells[][]Passed

SHA-256 / 3193c32390a61f4ac5d701d90f4260f799b99d5fc0ce92b4ec9da67e6de79281

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(rows, margins):
    return [round(w*margins[c]/sum(v for k,v in rows if k==c),8) for c,w in rows]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('unequal base weights',solve([('a',N),('a',3*N)],{'a':8*N}),[2*N,6*N])
check('separate margins',solve([('a',N),('b',N)],{'a':2*N,'b':6*N}),[2*N,6*N])
check('already calibrated',solve([('a',N),('a',3*N)],{'a':4*N}),[N,3*N])
check('single observation',solve([('a',N)],{'a':7*N}),[7*N])
check('row order preserved',solve([('b',N),('a',N),('b',3*N)],{'a':2*N,'b':8*N}),[2*N,2*N,6*N])
check('empty cells',solve([],{}),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
unequal base weights[2.0, 6.0][2, 6]Passed
separate margins[2.0, 6.0][2, 6]Passed
already calibrated[1.0, 3.0][1, 3]Passed
single observation[7.0][7]Passed
row order preserved[2.0, 2.0, 6.0][2, 2, 6]Passed
empty cells[][]Passed

SHA-256 / bd4ff405ceae221b6bcd86ac59d8bc6b1c70c757c058f8736fdc283e8366d30d

Verification & scope

Controlled finite fixtures; not a general survey-analysis package. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:38:58.585259+00:00.

Case digest / af9ca2dcec14aefc7d397bbc748e1d3d958c70fe9fa4e82efe2d1c1b20218a1e