Cover Image

Assumption-Lean Differential Variance Inference for Heterogeneous Treatment Effect Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Boileau, Philippe A., Zaki, Hani, Lileikyte, Gabriele, Nielsen, Niklas, Lawler, Patrick R., Schnitzer, Mireille E.
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Assumption Lean Regression
by: Berk, Richard A., et al.
Published: (2018)

Assumption-Lean and Data-Adaptive Post-Prediction Inference
by: Miao, Jiacheng, et al.
Published: (2023)

Guidance on Individualized Treatment Rule Estimation in High Dimensions
by: Boileau, Philippe, et al.
Published: (2023)

Assumption-Lean Quantile Regression
by: Baklicharov, Georgi, et al.
Published: (2024)

Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)

Dependable Exploitation of High-Dimensional Unlabeled Data in an Assumption-Lean Framework
by: Ying, Chao, et al.
Published: (2026)

Robust Mendelian Randomization Estimation using Weighted Quantile Regression
by: St-Pierre, Julien, et al.
Published: (2026)

A general framework for formulating structured variable selection
by: Wang, Guanbo, et al.
Published: (2021)

Bayesian Causal Synthesis for Meta-Inference on Heterogeneous Treatment Effects
by: Sugasawa, Shonosuke, et al.
Published: (2023)

Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference
by: Zhou, Lingxiao, et al.
Published: (2024)

Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
by: Scheidegger, Cyrill, et al.
Published: (2025)

Propensity Patchwork Kriging for Scalable Inference on Heterogeneous Treatment Effects
by: Ogawa, Hajime, et al.
Published: (2025)

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data
by: Jiang, Cong, et al.
Published: (2023)

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
by: Fisher, Aaron
Published: (2023)

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
by: Kacprzyk, Krzysztof, et al.
Published: (2024)

Is checking for sequential positivity violations getting you down? Try sPoRT!
by: Chatton, Arthur, et al.
Published: (2024)

Valid post-selection inference for penalized G-estimation
by: Jaman, Ajmery, et al.
Published: (2025)

Variance Estimation for Weighted Average Treatment Effects
by: Li, Huiyue, et al.
Published: (2025)

Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model
by: Wibisono, Kevin Christian, et al.
Published: (2025)

trajmsm: An R package for Trajectory Analysis and Causal Modeling
by: Diop, Awa, et al.
Published: (2024)

Integrating complex selection rules into the latent overlapping group Lasso for constructing coherent prediction models
by: Wang, Guanbo, et al.
Published: (2022)

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)

Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
by: Jaman, Ajmery, et al.
Published: (2024)

What if we had built a prediction model with a survival super learner instead of a Cox model 10 years ago?
by: Chatton, Arthur, et al.
Published: (2024)

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
by: Chikahara, Yoichi, et al.
Published: (2024)

Intra-Class Correlation Coefficient Ignorable Clustered Randomized Trials for Detecting Treatment Effect Heterogeneity
by: Yang, Chen, et al.
Published: (2025)

From Estimands to Robust Inference of Treatment Effects in Platform Trials
by: Qian, Yuhan, et al.
Published: (2024)

Variance Estimation with Dependence and Heterogeneous Means
by: Yap, Luther
Published: (2026)

Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity
by: Sechidis, Konstantinos, et al.
Published: (2025)

Identifying Treatment Effect Heterogeneity with Bayesian Hierarchical Adjustable Random Partition in Adaptive Enrichment Trials
by: Zhao, Xianglin, et al.
Published: (2025)

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
by: Persson, Joel, et al.
Published: (2026)

Assumption-lean Inference for Network-linked Data
by: Li, Wei, et al.
Published: (2025)

Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
by: Kubota, Kohsuke, et al.
Published: (2025)

Bridging Structural Causal Inference and Machine Learning The S-DIDML Estimator for Heterogeneous Treatment Effects
by: Yu, Yile, et al.
Published: (2025)

Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes
by: Chatton, Arthur, et al.
Published: (2023)

Joint Estimation of Marginal and Heterogeneous Treatment Effects
by: Wuethrich, Leticia, et al.
Published: (2026)

Evaluating and Testing for Actionable Treatment Effect Heterogeneity
by: Ashouri, Mahsa, et al.
Published: (2025)

On Weighted Orthogonal Learners for Heterogeneous Treatment Effects
by: Morzywolek, Pawel, et al.
Published: (2023)

Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond
by: Morzywolek, Pawel, et al.
Published: (2025)

Targeted Learning on Variable Importance Measure for Heterogeneous Treatment Effect
by: Li, Haodong, et al.
Published: (2023)