Cover Image

Identifying average causal effect in regression discontinuity design with auxiliary data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feng, Xinqin, Hu, Wenjie, Yang, Pu, Li, Tingyu, Zhou, Xiao-Hua
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

Identifiability of causal effects with non-Gaussianity and auxiliary covariates
by: Shuai, Kang, et al.
Published: (2023)

Improved inference for nonparametric regression and regression-discontinuity designs
by: Cavaliere, Giuseppe, et al.
Published: (2025)

Bayesian regression discontinuity design with unknown cutoff
by: Kowalska, Julia, et al.
Published: (2024)

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

Identification and multiply robust estimation of causal effects via instrumental variables from an auxiliary population
by: Li, Wei, et al.
Published: (2024)

Optimal estimation for regression discontinuity design with binary outcomes
by: Ishihara, Takuya, et al.
Published: (2025)

Potential weights and implicit causal designs in linear regression
by: Chen, Jiafeng
Published: (2024)

Robust inference for geographic regression discontinuity designs: assessing the impact of police precincts
by: Kendall, Emmett B., et al.
Published: (2021)

Absolute average and median treatment effects as causal estimands on metric spaces
by: Shin, Ha-Young, et al.
Published: (2024)

Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation
by: Ballerini, Veronica, et al.
Published: (2023)

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

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

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

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

Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect
by: von Felten, Stefanie, et al.
Published: (2023)

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects
by: Nguyen, Trang Quynh, et al.
Published: (2023)

Unifying regression-based and design-based causal inference in time-series experiments
by: Lin, Zhexiao, et al.
Published: (2025)

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

Semi-supervised learning using copula-based regression and model averaging
by: Gao, Ziwen, et al.
Published: (2024)

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
by: Xiong, Wei, et al.
Published: (2025)

Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
by: Kabata, Daijiro, et al.
Published: (2025)

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

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

Regression discontinuity design in perinatal epidemiology and birth cohort research
by: Popovic, Maja, et al.
Published: (2022)

Efficient collaborative learning of the average treatment effect
by: Li, Sijia, et al.
Published: (2024)

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions
by: Nguyen, Trang Quynh, et al.
Published: (2023)

Convolution-smoothing based locally sparse estimation for functional quantile regression
by: Liu, Hua, et al.
Published: (2025)

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

Augmented match weighted estimators for average treatment effects
by: Xu, Tanchumin, et al.
Published: (2023)

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding
by: Min, Jiaqi, et al.
Published: (2025)

Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
by: Zhu, Haiyan, et al.
Published: (2026)

Renewable estimation in linear expectile regression models with streaming data sets
by: Cao, Wei, et al.
Published: (2026)

Bounds for causal mediation effects
by: Breum, Marie S., et al.
Published: (2025)

A fine-grained look at causal effects in causal spaces
by: Park, Junhyung, et al.
Published: (2025)

Do-search -- a tool for causal inference and study design with multiple data sources
by: Karvanen, Juha, et al.
Published: (2020)

Modifying causal models to distinguish between transient and lasting causal effects
by: Steele, Russell, et al.
Published: (2026)

Statistics of spatial averages and optimal averaging in the presence of missing data
by: Seshadri, Ashwin K
Published: (2017)

Data adaptive covariate balancing for causal effect estimation for high dimensional data
by: De, Simion, et al.
Published: (2025)

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

Robust subgroup-classifier learning and testing in change-plane regressions
by: Liu, Xu, et al.
Published: (2024)