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

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Ignoring missing data in multi-armed bandit algorithms impacts performance differently based on exploration-exploitation strategies. Exploration-focused algorithms may worsen with missing data, but simple imputation can help.

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Area of Science:

  • Statistics
  • Machine Learning
  • Clinical Trials

Background:

  • Missing data is a common challenge in real-world applications of multi-armed bandit algorithms.
  • The impact of missing outcomes on algorithm performance is often underestimated.
  • Ignoring missing data is a simple, yet potentially problematic, implementation strategy.

Purpose of the Study:

  • To investigate the performance implications of ignoring missing data in multi-armed bandit algorithms.
  • To analyze how different exploration-exploitation trade-offs affect performance under missingness.
  • To evaluate mitigation strategies for missing data in bandit algorithms.

Main Methods:

  • Extensive simulation study of two-armed bandit algorithms with binary outcomes.
  • Analysis of algorithms under various probabilities of missing data in rewards.
  • Assessment of operating characteristics, including expected reward.

Main Results:

  • The impact of ignoring missing data on expected performance varies significantly with the algorithm's exploration-exploitation balance.
  • Exploration-heavy algorithms tend to exacerbate issues with missing data by favoring arms with less observed information.
  • Exploitation-focused algorithms prioritize arms with high observed means, regardless of missing data levels.

Conclusions:

  • The simplest strategy of ignoring missing data can negatively affect multi-armed bandit performance, particularly for exploration-oriented algorithms.
  • Mean imputation can effectively alleviate performance degradation caused by missing responses in exploration-focused algorithms.
  • Findings are relevant for patient allocation in clinical trials and other applications with missing outcomes.