Cover Image

Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Bingkai, Li, Fan, Yu, Mengxin
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How to achieve model-robust inference in stepped wedge trials with model-based methods?
by: Wang, Bingkai, et al.
Published: (2024)

Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
by: Fang, Xi, et al.
Published: (2025)

Model-robust standardization in cluster-randomized trials
by: Li, Fan, et al.
Published: (2025)

Handling incomplete outcomes and covariates in cluster-randomized trials: doubly-robust estimation, efficiency considerations, and sensitivity analysis
by: Wang, Bingkai, et al.
Published: (2024)

Asymptotic inference with flexible covariate adjustment under rerandomization and stratified rerandomization
by: Wang, Bingkai, et al.
Published: (2024)

Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials
by: Fang, Xi, et al.
Published: (2026)

On robust Bayesian causal inference
by: Alexopoulos, Angelos, et al.
Published: (2025)

Model-assisted inference for dynamic causal effects in staggered rollout cluster randomized experiments
by: Chen, Xinyuan, et al.
Published: (2025)

Statistical inference with win statistics in cluster-randomized trials with composite outcomes
by: Fang, Xi, et al.
Published: (2026)

Optimizing precision in stepped-wedge designs via machine learning and quadratic inference functions
by: Lyu, Liangbo, et al.
Published: (2026)

Conformal inference for random objects
by: Zhou, Hang, et al.
Published: (2024)

Bayesian inference for cluster-randomized trials with multivariate outcomes subject to both truncation by death and missingness
by: Tong, Guangyu, et al.
Published: (2025)

Design-based inference for generalized causal effects in randomized experiments
by: Chen, Xinyuan, et al.
Published: (2026)

Causal mediation in cluster-randomized trials with multiple mediators: spillover-aware decomposition, identification, and semiparametric efficient inference
by: Tong, Jiaqi, et al.
Published: (2026)

Generalizing and transporting causal inferences from randomized trials in the presence of trial engagement effects
by: Ung, Lawson, et al.
Published: (2024)

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures
by: Ouyang, Yongdong, et al.
Published: (2023)

Doubly-robust and heteroscedasticity-aware sample trimming for causal inference
by: Khan, Samir, et al.
Published: (2022)

Semiparametric causal mediation analysis of cluster-randomized trials for indirect and spillover effects
by: Cheng, Chao, et al.
Published: (2024)

Identification and efficient estimation of compliance and network causal effects in cluster-randomized trials
by: Cheng, Chao, et al.
Published: (2025)

Design-based theory for causal inference
by: Lu, Xin, et al.
Published: (2025)

Joint mixed-effects models for causal inference in clustered network-based observational studies
by: McNealis, Vanessa, et al.
Published: (2024)

Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference
by: Jiang, Zhichao, et al.
Published: (2025)

Optimal estimation of generalized causal effects in cluster-randomized trials with multiple outcomes
by: Chen, Xinyuan, et al.
Published: (2026)

Sample size and power calculations for causal inference of observational studies
by: Liu, Bo, et al.
Published: (2025)

Confounder-robust causal discovery and inference in Perturb-seq using proxy and instrumental variables
by: Park, Kwangmoon, et al.
Published: (2026)

Cluster randomized trials designed to support generalizable inferences
by: Robertson, Sarah E., et al.
Published: (2022)

Inverting estimating equations for causal inference on quantiles
by: Cheng, Chao, et al.
Published: (2024)

Credible causal inference beyond toy models
by: Bastías, Pablo Geraldo
Published: (2024)

Sample size and power calculations for causal inference with time-to-event outcomes
by: Yang, Chengxin, et al.
Published: (2026)

Linear models for causal inference under network interference
by: Tong, Eric, et al.
Published: (2026)

Leveraging specificity for causal inference in observational studies
by: Miao, Wang
Published: (2025)

A Bayesian approach to the survivor average causal effect in cluster-randomized crossover trials
by: Isenberg, Dane, et al.
Published: (2025)

Model-robust standardization in stepped wedge designs
by: Fang, Xi, et al.
Published: (2025)

Inverse regression for causal inference with multiple outcomes
by: Zhang, Wei, et al.
Published: (2025)

Bipartite causal inference with interference, time series data, and a random network
by: Song, Zhaoyan, et al.
Published: (2024)

On a new robust method of inference for general time series models
by: Wang, Zihan, et al.
Published: (2025)

Identification and estimation of causal mechanisms in cluster-randomized trials with post-treatment confounding using Bayesian nonparametrics
by: Ohnishi, Yuki, et al.
Published: (2025)

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

Tunable robustness in power-law inference
by: Lin, Qianying, et al.
Published: (2023)

Post-selection inference for causal effects after causal discovery
by: Chang, Ting-Hsuan, et al.
Published: (2024)