Claim Verify Loop

A claim-by-claim adversarial verification loop over a results draft. The artifact is the draft; the feedback signal is the count of unverified claims — claims not yet checked, or checked but not yet survived a stress test. You drive it to zero: each claim ends verified (reproduces and survives the obvious threats) or appropriately qualified (hedged, scoped, or retracted with the reason).

The discipline: a number that merely reproduces is not trustworthy — most wrong findings reproduce fine. A claim is verified only when it also survives the threat most likely to kill it: an outlier, a confound, a subgroup too small to mean anything, a sign that flips under stratification. This loop is a gate on an existing draft, not a generator of new findings.

When to use

Use this when you have a draft (or a list of claims) drawn from a dataset and want each claim red-teamed before it goes out. Default to verifying every discrete claim in the draft; if the user flags a few high-stakes claims, prioritize those but still sweep the rest. Not for open-ended discovery of new findings (that is the data-analysis loop) and not for diagnosing one known anomaly.

Setup

Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely value for each binding and present it as the recommended option; on other hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format: examples/run.example.yaml) and confirm the values before creating any other files.

Check snippets run in the user's environment via <analysis_cmd>, so they may use whatever the user has installed. Keep helper code stdlib-first (csv, statistics): if a snippet needs pandas/numpy, probe with try/except ImportError and degrade to a stdlib path, or offer a consented uv pip install "pandas==<ver>" — never assume the package is installed.

The loop

Copy this checklist and tick items off:

• [ ] Iteration 0 — extract the discrete checkable claims from <draft>; record nothing as verified.
• [ ] Pick one unverified claim.
• [ ] Reproduce its exact number from <dataset>; if it does not reproduce → refuted.
• [ ] Stress-test against the threat(s) most likely to kill it; classify verified / fragile.
• [ ] Revise <report>: keep verified, hedge/scope/retract fragile, correct refuted.
• [ ] Append a ledger row; the claim leaves the unverified set. Stop when none remain or at <budget>.

Iteration 0 — extract claims. Read <draft> and list its discrete, checkable claims, each with the number/effect it asserts and its claim type (a group difference, a correlation, a causal/policy claim, a subgroup result, a rate). These are the live unverified set. If the draft is prose, splitting it into discrete claims is the first job.

Then, until stop (all claims resolved, or budget):

1. Pick one unverified claim.
2. Reproduce — the first gate. Write <sandbox_root>/iter<N>/check.py to recompute the exact

statistic the claim states from <dataset>. Run it with <analysis_cmd>, redirecting output to <sandbox_root>/iter<N>/out.txt (never flood your context). If the number does not reproduce → refuted (the number is wrong); skip to step 4.

1. Stress-test — the second gate. Hit the claim with the one or two threats most likely to kill it

for its claim type:

• Outlier sensitivity — recompute dropping extreme points / using a robust statistic. Does the

effect survive, or was it driven by a handful of rows?

• Confound & Simpson's reversal — stratify by the obvious confounder; does the effect hold

within strata, or flip? A causal/policy claim that reverses within subgroups is not supported.

• Subgroup size & multiplicity — how large is the subgroup? Is the result one of many

comparisons? A striking rate on n=5 is noise.

• Alternative specification — a defensible different cut (different bins, controlling for a

covariate). Does the sign/size stay?

Classify: verified (reproduces and survives) or fragile (reproduces but collapses or flips under a reasonable stress). A claim whose number reproduces but whose implied interpretation is not supported — a descriptive gap dressed up as causal ("treatment works"), a tiny-n rate sold as "superior", a one-point correlation called an "early-warning signal" — is fragile, not verified: the statistic is fine, the conclusion drawn from it is not.

1. Revise the draft. Update <report>:

• verified → keep the claim, noting the robustness check it passed.
• fragile → hedge, scope, or down-weight it to what the data supports (e.g. "descriptively

higher, but the within-stratum comparison reverses — not evidence the treatment causes recovery"), or retract it. Never leave a fragile claim standing as first written.

• refuted → correct it with the right number, or remove it.

Record the verdict and the evidence.

1. Log one ledger row and continue; the claim leaves the unverified set.

Ledger

<sandbox_root>/ledger.tsv, tab-separated, never commas in the text. Header:

iter	claim	verdict	threat	resolution

verdict ∈ {extract, verified, fragile, refuted}. Example:

iter	claim	verdict	threat	resolution
0	claims extracted	extract	-	7 claims listed
1	treatment recovery rate > control (70.6 vs 55.0)	verified	reproduced; holds	kept
2	treatment causes higher recovery (+16pp)	fragile	Simpson: control >= treatment within both age groups	rescoped to descriptive; causal claim retracted
3	biomarker correlates with recovery_days (r=0.16)	fragile	one outlier drives it (r=0.16 -> 0.02 without it)	retracted
5	pilot site 100% recovery (superior)	fragile	n=5 subgroup	hedged: too small to conclude

Report the outcome: the <report> path, the per-claim verdicts, and a summary — how many claims were verified, hedged, or retracted, and the single most important fragility found.

Constraints

• *Reproduce and stress-test — both.* A claim that only reproduces is not verified; it must

survive the threat most likely to kill it. Skipping the stress test is the failure mode this loop exists to prevent.

• Every verdict is backed by a re-run recorded in <report>; no claim is waved through or

condemned on intuition.

• A fragile claim is changed, never left standing — hedge it to what the data supports or retract

it, because leaving it as first written is exactly what shipped the unverified draft.

• Distinguish description from causation — "treatment arm recovered more" can be true while

"treatment causes recovery" is refuted by a confound; say exactly what the data supports.

• Only read `<dataset>` — never modify it, because it is the ground truth every claim is checked

against. The sandbox is self-contained (no ../ escapes).

• One claim per iteration, so each verdict is attributable.
• Do not pause the loop to ask whether to continue; run until all claims are resolved or <budget>.

Stops

• Resolved — no unverified or unresolved-fragile claims remain.
• Budget — <budget> iterations reached.