:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ziersen, Simon Christoffer, Martinussen, Torben
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	https://arxiv.org/abs/2412.11790
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variable importance measures for heterogeneous treatment effects with survival outcome
by: Simon Christoffer Ziersen, et al.
Published: (2025)

Causal effect on the number of life years lost due to a specific event: Average treatment effect and variable importance
by: Ziersen, Simon Christoffer, et al.
Published: (2025)

Debiased inference for stochastic treatment interventions with survival outcomes
by: Martinussen, Torben, et al.
Published: (2026)

Efficient nonparametric estimators of discrimination measures with censored survival data
by: Breum, Marie Skov, et al.
Published: (2024)

Variable importance measures for heterogeneous treatment effects
by: Hines, Oliver J., et al.
Published: (2022)

Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
by: Sun, Yuming, et al.
Published: (2026)

Nonparametric estimation of the Patient Weighted While-Alive Estimand
by: Ragni, Alessandra, et al.
Published: (2024)

On the limitations for causal inference in Cox models with time-varying treatment
by: Knudsen, Mark B., et al.
Published: (2025)

Nonparametric efficient estimation of the longitudinal front-door functional
by: Breum, Marie S., et al.
Published: (2025)

A joint QoL-Survival framework with debiased estimation under truncation by death
by: Martinussen, Torben, et al.
Published: (2026)

A pseudo-outcome-based framework to analyze treatment heterogeneity in survival data using electronic health records
by: Bo, Na, et al.
Published: (2024)

A class of nonparametric methods for evaluating the effect of continuous treatments on survival outcomes
by: Jin, Yutong, et al.
Published: (2024)

Integrating tumor burden with survival outcome for treatment effect evaluation in oncology trials
by: Bhandari, Saurabh, et al.
Published: (2025)

Finding network effect of randomized treatment under weak assumptions for any outcome and any effect heterogeneity
by: Lee, Myoung-jae
Published: (2025)

Trustworthy assessment of heterogeneous treatment effect estimator
by: Gao, Zijun
Published: (2024)

Multi forests: Variable importance for multi-class outcomes
by: Hornung, Roman, et al.
Published: (2024)

Estimation of treatment effect among treatment responders with a time‐to‐event endpoint
by: Andreas Nordland, et al.
Published: (2024)

Estimation and inference of average treatment effects under heterogeneous additive treatment effect model
by: Lu, Xin, et al.
Published: (2024)

Assessing variable importance in survival analysis using machine learning
by: Wolock, Charles J., et al.
Published: (2023)

Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit
by: Li, Xingyu, et al.
Published: (2026)

An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes
by: She, Jane, et al.
Published: (2025)

Two-stage least squares with treatment-covariate interactions for treatment effect heterogeneity
by: Zhao, Anqi, et al.
Published: (2025)

Data fusion methods for the heterogeneity of treatment effect and confounding function
by: Yang, Shu, et al.
Published: (2020)

A causal fused lasso for interpretable heterogeneous treatment effects estimation
by: Padilla, Oscar Hernan Madrid, et al.
Published: (2021)

Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference
by: Huang, Melody, et al.
Published: (2025)

Doubly robust average treatment effect estimation for survival data
by: Lee, Byeonghee, et al.
Published: (2025)

Flexible survival regression with variable selection for heterogeneous population
by: Mandal, Abhishek, et al.
Published: (2024)

Propensity score weighted Cox regression for survival outcomes in observational studies with multiple or factorial treatments
by: Zhao, Zixian, et al.
Published: (2026)

Statistical inference of heterogeneous treatment effects using semiparametric single-index model
by: Yu, Jichang, et al.
Published: (2025)

Semi-supervised inference for treatment heterogeneity
by: Anniwaer, Yilizhati, et al.
Published: (2025)

Estimating heterogeneous treatment effects by W-MCM based on Robust reduced rank regression
by: Hieda, Ryoma, et al.
Published: (2024)

Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
by: Lipkovich, Ilya, et al.
Published: (2023)

Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
by: Imai, Kosuke, et al.
Published: (2025)

Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome
by: Zuo, Shuozhi, et al.
Published: (2026)

Identifying sparse treatment effects in high-dimensional outcome spaces
by: Jeong, Yujin, et al.
Published: (2024)

Transporting treatment effects by calibrating large-scale observational outcomes
by: Li, Harrison H
Published: (2026)

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
by: Karlsson, Rickard, et al.
Published: (2025)

Increased risk of type I errors for detecting heterogeneity of treatment effects in cluster-randomized trials using mixed-effect models
by: Hyun, Noorie, et al.
Published: (2024)

Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"
by: Chernozhukov, Victor, et al.
Published: (2025)

Bayesian analysis of regression discontinuity designs with heterogeneous treatment effects
by: Tao, Kevin, et al.
Published: (2025)