:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Balzer, Laura B., van der Laan, Mark J., Petersen, Maya L.
Format:	Preprint
Publié:	2025
Sujets:	Methodology Machine Learning
Accès en ligne:	https://arxiv.org/abs/2512.13610
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications
par: Nance, Nerissa, et autres
Publié: (2023)

Adaptive debiased machine learning using data-driven model selection techniques
par: van der Laan, Lars, et autres
Publié: (2023)

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
par: van der Laan, Lars, et autres
Publié: (2024)

Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE
par: Qiu, Sky, et autres
Publié: (2026)

Statistical learning for constrained functional parameters in infinite-dimensional models
par: Nabi, Razieh, et autres
Publié: (2024)

Practical considerations for specifying a super learner
par: Phillips, Rachael V., et autres
Publié: (2022)

An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
par: Zhang, Wenxin, et autres
Publié: (2024)

Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
par: van der Laan, Mark, et autres
Publié: (2024)

Hazard-Based Targeted Maximum Likelihood Estimation for Survival in Resampling Designs
par: Landsiedel, Kirsten E., et autres
Publié: (2025)

Calibeating Prediction-Powered Inference
par: van der Laan, Lars, et autres
Publié: (2026)

Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
par: van der Laan, Lars, et autres
Publié: (2025)

Self-Calibrating Conformal Prediction
par: van der Laan, Lars, et autres
Publié: (2024)

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
par: van der Laan, Lars, et autres
Publié: (2025)

Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies
par: Dang, Lauren Eyler, et autres
Publié: (2022)

Generative adversarial learning with optimal input dimension and its adaptive generator architecture
par: Tan, Zhiyao, et autres
Publié: (2024)

Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
par: Liang, Zhongyuan, et autres
Publié: (2025)

When exposure affects subgroup membership: Framing relevant causal questions in perinatal epidemiology and beyond
par: Gupta, Shalika, et autres
Publié: (2024)

Nonparametric efficient causal estimation of the intervention-specific expected number of recurrent events with continuous-time targeted maximum likelihood and highly adaptive lasso estimation
par: Rytgaard, Helene C. W., et autres
Publié: (2024)

Efficient Statistical Estimation for Sequential Adaptive Experiments with Implications for Adaptive Designs
par: Zhang, Wenxin, et autres
Publié: (2025)

Technical Note: Targeted Maximum Likelihood Estimator for an ATE Standardized for New Target Population
par: van der Laan, Mark, et autres
Publié: (2025)

Stabilized Inverse Probability Weighting via Isotonic Calibration
par: van der Laan, Lars, et autres
Publié: (2024)

Nonparametric Instrumental Variable Inference with Many Weak Instruments
par: van der Laan, Lars, et autres
Publié: (2025)

Doubly robust inference via calibration
par: van der Laan, Lars, et autres
Publié: (2024)

Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems
par: Qiu, Sky, et autres
Publié: (2026)

An adaptive design for optimizing treatment assignment in randomized clinical trials
par: Zhang, Wei, et autres
Publié: (2025)

Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection
par: van de Wiel, Mark A., et autres
Publié: (2024)

Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
par: van der Laan, Lars, et autres
Publié: (2025)

Minimax optimal adaptive structured transfer learning through semi-parametric domain-varying coefficient model
par: Chen, Hanxiao, et autres
Publié: (2026)

Improving randomized controlled trial analysis via data-adaptive borrowing
par: Gao, Chenyin, et autres
Publié: (2023)

Causal inference for calibrated scaling interventions on time-to-event processes
par: Rytgaard, Helene Charlotte Wiese, et autres
Publié: (2025)

Targeted Learning Estimation of Sampling Variance for Improved Inference
par: Ji, Yunwen, et autres
Publié: (2025)

Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
par: Boileau, Philippe, et autres
Publié: (2026)

Optimal differentially private kernel learning with random projection
par: Lee, Bonwoo, et autres
Publié: (2025)

Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
par: Balzer, Laura B., et autres
Publié: (2022)

Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
par: Xu, Yichen, et autres
Publié: (2026)

Gradient Boosting for Spatial Panel Models with Random and Fixed Effects
par: Balzer, Michael, et autres
Publié: (2026)

Semiparametric logistic regression for inference on relative vaccine efficacy in case-only studies with informative missingness
par: van der Laan, Lars, et autres
Publié: (2023)

Utility-based optimization of Fujikawa's basket trial design -- Pre-specified protocol of a comparison study
par: Sauer, Lukas D, et autres
Publié: (2024)

[Invited Discussion] Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
par: Phillips, Rachael V., et autres
Publié: (2024)

Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
par: Xu, Yichen, et autres
Publié: (2026)