Admissibility of Randomized Controlled Trials: A Large-Deviation Approach

Music City Center 

Randomized control trials (RCTs) are the gold standard for causal inference but can be inefficient compared to adaptive designs. A key question is whether adaptive designs can universally outperform RCTs without relying on problem-specific knowledge. We answer this affirmatively in the best-arm identification problem, showing that simple adaptive designs, which sequentially eliminate underperforming arms, universally strictly dominates standard RCTs when there are at least three treatment arms. This dominance is characterized by a notion called efficient exponent, which quantifies a design's statistical efficiency in large populations. Additionally, we derive the maximin elimination-based design, further highlighting that adaptive designs can potentially achieve both efficiency and robustness. This is a joint work with Guido Imbens and Stefan Wager.