pith:R6WWXKTU
Transporting treatment effects by calibrating large-scale observational outcomes
By calibrating observational outcome measurements to a small experimental dataset via ordinary least squares, researchers obtain a consistent estimator for the transported average treatment effect with valid inference even without overlap.
arxiv:2605.07285 v2 · 2026-05-08 · stat.ME
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Claims
our estimator is consistent for the transported average treatment effect. Otherwise, it converges to a projection estimand. As long as the observational dataset size grows sufficiently quickly relative to the experimental dataset size, our estimator achieves a notion of semiparametric efficiency proposed in recent work on semi-supervised learning for the projection estimand.
When the calibration regression is well specified for consistency; also that the observational dataset size grows sufficiently quickly relative to the experimental dataset size for efficiency, and that OLS calibration can handle biased measurements in the observational outcome.
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.
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| First computed | 2026-05-20T00:03:14.714087Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64 \
| 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: 8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d
Canonical record JSON
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