Pith Number

pith:Q5RMJMIF

pith:2026:Q5RMJMIFPGMS5GYYUAVOY3YSDV

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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials

Niwen Zhou, Peng Wu, Xu Guo, Ziyang Liu

A doubly robust estimator identifies quantile treatment effects on long-term outcomes by linking trial surrogates to external data.

arxiv:2605.14275 v1 · 2026-05-14 · math.ST · stat.TH

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Claims

C1strongest claim

Under treatment randomization, positivity, and a surrogate-based transportability assumption, we establish identification and develop a doubly robust estimator for inference. The estimator accommodates flexible machine learning methods for nuisance estimation, remains consistent if either the score-related or outcome regression-related nuisance functions are consistently estimated, and is asymptotically normal under regularity conditions.

C2weakest assumption

The surrogate-based transportability assumption that permits linking short-term surrogates observed in the randomized trial to long-term outcomes in the external observational data.

C3one line summary

A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.

References

33 extracted · 33 resolved · 0 Pith anchors

[1] Double/debiased machine learning for treatment and structural parameters , author=. 2018 , publisher= 2018

[2] Gaussian approximation of suprema of empirical processes , author=

[3] Handbook of econometrics , volume= 1994

[4] Weak convergence , author=. 1996 , publisher= 1996

[5] Inverting estimating equations for causal inference on quantiles , author=. Biometrika , volume=. 2025 , publisher= 2025

Receipt and verification

First computed	2026-05-17T23:39:10.350635Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8762c4b10579992e9b18a02aec6f121d6f4a2c53b9eec700869346f62acfa33a

Aliases

arxiv: 2605.14275 · arxiv_version: 2605.14275v1 · doi: 10.48550/arxiv.2605.14275 · pith_short_12: Q5RMJMIFPGMS · pith_short_16: Q5RMJMIFPGMS5GYY · pith_short_8: Q5RMJMIF

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q5RMJMIFPGMS5GYYUAVOY3YSDV \
  | 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: 8762c4b10579992e9b18a02aec6f121d6f4a2c53b9eec700869346f62acfa33a

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "ee19ff875ac76f6263b008403213cc73533f7cc1faa8116e2efb47d72658e6f3",
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      "stat.TH"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.ST",
    "submitted_at": "2026-05-14T02:19:41Z",
    "title_canon_sha256": "2b69707950ae304da74973cd88d48a2cd1dbdf667e2916317ad0636400470530"
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