246 citations · 274 across the 20 of their papers we have counts for
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Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
Tong Li, Jacob Nogas, Haochen Song +8
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engag…
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling
Nina Deliu, Joseph J. Williams, Sofia S. Villar
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated t…
Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model
Rob Johnson, Chris Jackson, Anne Presanis +2
Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect th…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…