Pith Number
pith:NQKPBO6U
pith:2026:NQKPBO6U5Q4H46S3UMZUL6EXWP
not attested
not anchored
not stored
refs pending
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment
arxiv:2607.07065 v1 · 2026-07-08 · stat.AP · stat.CO · stat.ME
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{NQKPBO6U5Q4H46S3UMZUL6EXWP}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-09T01:20:13.557429Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6c14f0bbd4ec387e7a5ba33345f897b3f70613c24466e503a783db99f5cc467c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/NQKPBO6U5Q4H46S3UMZUL6EXWP \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6c14f0bbd4ec387e7a5ba33345f897b3f70613c24466e503a783db99f5cc467c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "6131c8b4954d6239916cc96bc960e509b6339c208520989c4304665b0f8e7cd7",
"cross_cats_sorted": [
"stat.CO",
"stat.ME"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.AP",
"submitted_at": "2026-07-08T06:48:07Z",
"title_canon_sha256": "b5c694f5cee749f064351f0dbe4d9e62bbef8c9f1d54ee3d0a2f824e02478291"
},
"schema_version": "1.0",
"source": {
"id": "2607.07065",
"kind": "arxiv",
"version": 1
}
}