Cover Image

Design of egocentric network-based studies to estimate causal effects under interference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fang, Junhan, Spiegelman, Donna, Buchanan, Ashley, Forastiere, Laura
Format:	Preprint
Published:	2023
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Identifying Key Influencers using an Egocentric Network-based Randomized Design
by: He, Zhibing, et al.
Published: (2025)

Effective treatment allocation strategies under partial interference
by: Dean, Samantha G, et al.
Published: (2025)

Design-Based Weighted Regression Estimators for Average and Conditional Spillover Effects
by: Fang, Fei, et al.
Published: (2025)

Sensitivity analysis for contamination in egocentric-network randomized trials with interference
by: Weinstein, Bar, et al.
Published: (2026)

The causal effects of modified treatment policies under network interference
by: Balkus, Salvador V., et al.
Published: (2024)

Adaptive Weighted Random Isolation (AWRI): a simple design to estimate causal effects under network interference
by: Shi, Changhao, et al.
Published: (2024)

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

Inward and Outward Spillover Effects of One Unit's Treatment on Network Neighbors under Partial Interference
by: Fang, Fei, et al.
Published: (2025)

Nonparametric efficient inference for network quantile causal effects under partial interference
by: Cheng, Chao, et al.
Published: (2026)

Regression Discontinuity Designs Under Interference
by: Torrione, Elena Dal, et al.
Published: (2024)

Causal Covariate Selection for the Imputation-based Regression Calibration Method for Exposure Measurement Error Bias Correction
by: Tang, Wenze, et al.
Published: (2023)

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints
by: Owen, Melody, et al.
Published: (2025)

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome
by: Cheng, Chao, et al.
Published: (2023)

Improving Transportability of Regression Calibration Under the Main/External Validation Study Design
by: Li, Zexiang, et al.
Published: (2025)

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials
by: Bing, Ante, et al.
Published: (2025)

Statistical methods for cost-effectiveness analysis of left-truncated censored survival data with treatment delays
by: Khudyakov, Polyna, et al.
Published: (2025)

Forecasting Causal Effects of Future Interventions: Confounding and Transportability Issues
by: Forastiere, Laura, et al.
Published: (2024)

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure
by: Tang, Wenze, et al.
Published: (2022)

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

Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
by: Eichhorn, Matthew, et al.
Published: (2024)

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

A regression framework for studying relationships among attributes under network interference
by: Fritz, Cornelius, 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)

Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs
by: Forastiere, Laura, et al.
Published: (2022)

Transportability of Regression Calibration with External Validation Studies for Measurement Error Correction
by: Li, Zexiang, et al.
Published: (2026)

Optimized variance estimation under interference and complex experimental designs
by: Harshaw, Christopher, et al.
Published: (2021)

Efficient nonparametric estimation with difference-in-differences in the presence of network dependence and interference
by: Jetsupphasuk, Michael, et al.
Published: (2025)

Spatial causal inference in the presence of unmeasured confounding and interference
by: Papadogeorgou, Georgia, et al.
Published: (2023)

Exposure measurement error correction in longitudinal studies with discrete outcomes
by: Yang, Ce, et al.
Published: (2025)

Measurement Error Correction for Spatially Defined Environmental Exposures in Survival Analysis
by: Ge, Lin, et al.
Published: (2024)

Disentangling spatial interference and spatial confounding biases in causal inference
by: Ogunsola, Isqeel, et al.
Published: (2026)

Causal effect estimation under network interference with mean-field methods
by: Bhattacharya, Sohom, et al.
Published: (2024)

Calibrated Bayes analysis of cluster-randomized trials
by: Liu, Ruyi, et al.
Published: (2025)

Identification and estimation of causal peer effects using instrumental variables
by: Luo, Shanshan, et al.
Published: (2025)

Bayesian estimation of causal effects from observational categorical data
by: Kvisgaard, Vera, et al.
Published: (2025)

Nonparametric identification and efficient estimation of causal effects with instrumental variables
by: Levis, Alexander W., et al.
Published: (2024)

Estimating network-mediated causal effects via principal components network regression
by: Hayes, Alex, et al.
Published: (2022)

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

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

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