Pith Number

pith:NFHBXRYD

pith:2025:NFHBXRYDJSDS7HI6TQ657ACNW2

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Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules under Effect Heterogeneity

Eun Jeong Oh, Min Qian

Reluctant transfer learning updates individualized treatment rules under effect shifts by selective component transfer.

arxiv:2511.08559 v2 · 2025-11-11 · stat.ME

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Record completeness

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Claims

C1strongest claim

We propose a Reluctant Transfer Learning (RTL) framework that enables efficient model adaptation by selectively transferring essential model components (e.g., regression coefficients) from source to target data, without requiring access to individual-level source data... and provides a regret bound for the difference in value of the optimal ITR and that of the estimated ITR.

C2weakest assumption

The principle of reluctant modeling will only incorporate adjustments when they improve performance on the target dataset, which assumes that performance gains can be reliably detected without overfitting or post-hoc selection bias in the multi-armed treatment setting.

C3one line summary

A reluctant transfer learning method for penalized regressions adapts individualized treatment rules to shifted treatment-covariate relationships by selectively transferring coefficients and provides a regret bound.

References

4 extracted · 4 resolved · 0 Pith anchors

[1] Chu, J., Lu, W., & Yang, S. (2023). Targeted optimal treatment regime learning using summary statistics. Biometrika,110(4), 913–931. Dahabreh, I. J., Petito, L. C., Robertson, S. E., Hern´ an, M. A., 2023

[2] Mo, W., Qi, Z., & Liu, Y. (2021). Learning optimal distributionally robust individualized treatment rules. Journal of the American Statistical Association,116(534), 659–674. Murphy, S. A. (2003). Opti 2021

[3] Robertson, S. E., Steingrimsson, J. A., & Dahabreh, I. J. (2023). Regression-based estimation of heteroge- neous treatment effects when extending inferences from a randomized trial to a target populat 2023

[4] Zou, H. (2006). The adaptive lasso and its oracle properties.Journal of the American statistical association, 101(476), 1418–1429. 13 Zou, H., & Zhang, H. H. (2009). On the adaptive elastic-net with a 2006

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:17.139636Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

694e1bc7034c872f9d1e9c3ddf804db6b0073725a08c7ea75632ea0230136727

Aliases

arxiv: 2511.08559 · arxiv_version: 2511.08559v2 · doi: 10.48550/arxiv.2511.08559 · pith_short_12: NFHBXRYDJSDS · pith_short_16: NFHBXRYDJSDS7HI6 · pith_short_8: NFHBXRYD

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NFHBXRYDJSDS7HI6TQ657ACNW2 \
  | 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: 694e1bc7034c872f9d1e9c3ddf804db6b0073725a08c7ea75632ea0230136727

Canonical record JSON

{
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    "abstract_canon_sha256": "fe12b2e1c1c3b9a156e35bf17aca288d3e02ba92fc440f3bcb1527cfb847b2d6",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2025-11-11T18:41:50Z",
    "title_canon_sha256": "8a0951fa770dc0af01c2719ab7058f9875a302ff4810a687de865a4215297884"
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    "kind": "arxiv",
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