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ARXIV:2604.21260 · STATISTICAL INFERENCE · SUBMITTED 24 APR · 20:31 UTC · FRESHNESS STALE
ARXIV:2604.21260STATISTICAL INFERENCESUBMITTED 24 APR · 20:31 UTCFRESHNESS STALELars van der Laan · Mark Van Der Laan · arXiv
A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency.
Opportunity summary
Pain A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against…
We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment…
Statistical Inference moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency.
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10.48550/arXiv.2604.21260A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency.
Abstract
We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first-order optimality guarantees: isotonic post-processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post-processing rules, while no further post-processing of the fitted isotonic score yields additional first-order gains. For linear calibration, we show first-order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real-data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++. We provide an accompanying Python package, ppi_aipw, at https://larsvanderlaan.github.io/ppi-aipw/.
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PROBLEM
A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspeci...
METHOD
We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which p...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. Code availability is flagged in t...
WHY NOW
Statistical Inference moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Lemma 4 (Isotonic L2 rate). Suppose Assumptions 1 and 2 holds. Then ∥m⋆n,iso − m0∥22,P0,X = Op(n−2/3). Proof of Lemma 4. Let T := m(X) and let MC := {f(T) : f ∈ Fiso, ∥f(T)∥∞ ≤ C}. By Assumption 2
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A Python package for semisupervised mean estimation that calibrates prediction models to improve accuracy and efficiency.
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