Efficient Estimation of the Complier General Causal Effect in Randomized Controlled Trials with One-Sided Noncompliance

2025/10/15 by Tang, Yin, Ma, Yanyuan, Zhao, Jiwei
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2510.14142

Abstract

A randomized controlled trial (RCT) is widely regarded as the gold standard for assessing the causal effect of a treatment or intervention, assuming perfect implementation. In practice, however, randomization can be compromised for various reasons, such as one-sided noncompliance. In this paper, we address the issue of one-sided noncompliance and propose a general estimand, the complier general causal effect (CGCE), to characterize the causal effect among compliers. We further investigate the conditions under which efficient estimation of the CGCE can be achieved under minimal assumptions. Comprehensive simulation studies and a real data application are conducted to illustrate the proposed methods and to compare them with existing approaches.

Citations

• An Introduction to Double/Debiased Machine Learning
• Doubly-robust inference and optimality in structure-agnostic models with smoothness
• Nonparametric identification and efficient estimation of causal effects with instrumental variables
• Noncompliance in randomized control trials without exclusion restrictions
• Semiparametric theory and empirical processes in causal inference
• Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

Related

• Estimating the Complier Average Causal Effect in Randomised Controlled Trials with Non-Compliance: A Comparative Simulation Study of the Instrumental Variables and Per-Protocol Analyses
• Inference of Sample Complier Average Causal Effects in Completely Randomized Experiments
• Synthetic estimation for the complier average causal effect
• Inference of Sample Complier Average Causal Effects under Experiments with Completely Randomized Design and Computer Assisted Balance-Improving Designs
• Instrumental variable approaches for estimating complier average causal\n effects on bivariate outcomes in randomised trials with non-compliance