Balancing Efficiency and Feasibility: A Sensitivity Analysis of the Augmentation Parameter in the Finite Selection Model

TL;DR

This study examines how the augmentation parameter affects the Finite Selection Model's estimator performance, revealing that moderate augmentation balances covariate balance and efficiency, while excessive augmentation can harm stability.

Contribution

It provides a comprehensive sensitivity analysis of the augmentation parameter in FSM, offering practical guidelines for optimal parameter selection.

Findings

Moderate augmentation improves covariate balance.

Excessive augmentation increases variance.

Guidelines for selecting augmentation parameters in practice.

Abstract

This paper investigates the role of the augmentation parameter in the Finite Selection Model (FSM) and its impact on estimator performance. Through a comprehensive Monte Carlo simulation study, we analyze the sensitivity of bias, variance, and mean squared error to different values of the augmentation parameter. The results demonstrate that moderate augmentation improves covariate balance while maintaining estimation efficiency. However, excessive augmentation may increase variance and reduce estimator stability. The findings provide practical guidelines for selecting the augmentation parameter in applied experimental design settings.