论文检索

结局均值函数一般半参数约束下的有效估计与数据融合AI 解读

We provide a novel characterization of semiparametric efficiency in a generic supervised learning setting where the outcome mean function -- defined as the conditional expectation of the outcome of interest given the other observed variables -- is restricted to lie in some known semiparametric function class. The primary motivation is causal inference where a researcher running a randomized controlled trial often has access to an auxiliary observational dataset that is…

如何设定在线 A/B 实验的持续时间AI 解读

In designing an online A/B experiment, it is crucial to select a sample size and duration that ensure the resulting confidence interval (CI) for the treatment effect is the right width to detect an effect of meaningful magnitude with sufficient statistical power without wasting resources. While the relationship between sample size and CI width is well understood, the effect of experiment duration on CI width remains less clear. This paper provides an analytical formula for…

批次自适应实验中的双重机器学习与实验设计AI 解读

We consider an experiment with at least two stages or batches and $O(N)$ subjects per batch. First, we propose a semiparametric treatment effect estimator that efficiently pools information across the batches, and show it asymptotically dominates alternatives that aggregate single batch estimates. Then, we consider the design problem of learning propensity scores for assigning treatment in the later batches of the experiment to maximize the asymptotic precision of this…