Adaptive Treatment Assignment in Experiments for Policy Choice
提出一种名为“探索抽样”的自适应分配算法，用于多波次实验中动态分配处理，以在实验结束时选出最优政策。该算法在渐近最优性和福利改善上优于传统设计和赌博机算法，并通过印度农业推广服务的六种招募策略选择案例验证了可行性。
Standard experimental designs are geared toward point estimation and hypothesis testing, while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider treatment assignment in an experiment with several waves for choosing the best among a set of possible policies (treatments) at the end of the experiment. We propose a computationally tractable assignment algorithm that we call “exploration sampling,” where assignment probabilities in each wave are an increasing concave function of the posterior probabilities that each treatment is optimal. We prove an asymptotic optimality result for this algorithm and demonstrate improvements in welfare in calibrated simulations over both non‐adaptive designs and bandit algorithms. An application to selecting between six different recruitment strategies for an agricultural extension service in India demonstrates practical feasibility.