Confidence Intervals for Policy Evaluation in Adaptive Experiments
自适应实验中政策评估的置信区间AI 解读 ↓
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自适应实验中政策评估的置信区间
- 研究问题
- 在分配概率随实验结果调整、甚至趋近于零的实验中，如何评估非设计目标的处理或政策价值，并获得可靠置信区间？
- 方法与思路
- 作者对增广逆概率加权估计的各项进行自适应重加权，控制每项对方差的贡献，以缓解分布偏斜和重尾问题。
- 材料陈述的贡献
- 摘要称，自适应权重可保证渐近覆盖并降低方差，尤其有助于非设计目标的假设检验；数值实验比较了误差和覆盖率。
- 阅读关注点
- 应核对目标政策价值、权重构造及分配概率衰减的限制，检查重加权是否改变估计目标，以及渐近覆盖的正则条件。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或被期刊接收；摘要未说明概率衰减条件及具体实验比较结果。
原文摘要
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges are exacerbated when our parameter of interest differs from the parameter the trial was designed to target, such as when we are interested in estimating the value of a sub-optimal treatment after running a trial to determine the optimal treatment using a stochastic bandit design. In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero. In this paper, we present a class of estimators that overcome these issues. Our approach is to adaptively reweight the terms of an augmented inverse propensity weighting estimator to control the contribution of each term to the estimator's variance. This adaptive weighting scheme prevents estimates from becoming heavy-tailed, ensuring asymptotically correct coverage. It also reduces variance, allowing us to test hypotheses with greater power - especially hypotheses that were not targeted by the experimental design. We validate the accuracy of the resulting estimates and their confidence intervals in numerical experiments and show our methods compare favorably to existing alternatives in terms of RMSE and coverage.