Assumption-lean covariate adjustment under covariate adaptive randomization when $p = o (n)$
Published:Dec 23, 2025 04:40
•1 min read
•ArXiv
Analysis
This article likely discusses statistical methods for clinical trials or experiments. The focus is on adjusting for covariates (variables that might influence the outcome) in a way that makes fewer assumptions about the data, especially when the number of covariates (p) is much smaller than the number of observations (n). This is a common problem in fields like medicine and social sciences where researchers want to control for confounding variables without making overly restrictive assumptions about their relationships.
Key Takeaways
- •Focuses on statistical methods for covariate adjustment.
- •Addresses scenarios where the number of covariates is smaller than the number of observations.
- •Aims to make fewer assumptions about the data.
- •Relevant to fields like medicine and social sciences.
Reference
“The title suggests a focus on statistical methodology, specifically covariate adjustment within the context of randomized controlled trials or similar experimental designs. The notation '$p = o(n)$' indicates that the number of covariates is asymptotically smaller than the number of observations, which is a common scenario in many applications.”