Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

TL;DR

This study examines how ignoring missing data affects the performance of multi-armed bandit algorithms in clinical trial simulations, revealing that the impact varies with the exploration-exploitation balance and can be mitigated by simple imputation.

Contribution

It provides an extensive simulation analysis of missing data effects on different bandit algorithms, highlighting the importance of handling missingness appropriately in practice.

Findings

Ignoring missing data impacts algorithms differently based on their exploration-exploitation focus.

Exploration-oriented algorithms tend to over-sample arms with missing responses.

Simple mean imputation can mitigate missing data issues for exploration-focused algorithms.

Abstract

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.