Simulation-Based Inference for Adaptive Experiments

This paper introduces a simulation-based method for statistical inference in adaptive experiments, specifically addressing challenges that arise when analyzing data from multi-arm bandit designs. Unlike traditional randomized trials, adaptive designs modify treatment assignments during the experiment, which can complicate standard inference techniques. The proposed approach, called simulation with optimism, generates artificial experiment trajectories under a null hypothesis by adding a slight positive bias to estimated parameters. The authors demonstrate that this method provides asymptotic control over Type I error and produces confidence intervals with significantly reduced widths, particularly for treatments that were not prioritized by the adaptive sampling strategy. Empirical results on both simulated and real-world data support the effectiveness and computational feasibility of this simulation-based inference technique.