pith:NKEHYCHX
You've Got to be Efficient: Ambiguity, Misspecification and Variational Preferences
Optimal decisions for estimation and treatment assignment coincide with those under correct specification regardless of misspecification degree.
arxiv:2604.05327 v3 · 2026-04-07 · econ.EM
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Record completeness
Claims
for both estimation and treatment assignment, we show that optimal decisions coincide with those under correct specification, regardless of the degree of misspecification. These results extend to semi-parametric models.
The framework assumes that uniformly expanding the ambiguity set by a Kullback-Leibler radius adequately captures likelihood misspecification while allowing separation from prior ambiguity, and that the resulting minimax problem with exponentially tilted loss yields decisions equivalent to the correctly specified case.
Optimal decisions under combined prior ambiguity and likelihood misspecification coincide with those under correct specification for estimation and treatment assignment, regardless of misspecification degree.
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Receipt and verification
| First computed | 2026-06-09T01:05:16.885928Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6a887c08f7b7c0e55fa4a3b07bdfc624f97306eb193fb203f057ac3c74411841
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/NKEHYCHXW7AOKX5EUOYHXX6GET \
| 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: 6a887c08f7b7c0e55fa4a3b07bdfc624f97306eb193fb203f057ac3c74411841
Canonical record JSON
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