论文检索

基于实验设计的置信序列：面板实验中的风险缓解AI 解读

Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. Beyond aiding managerial decision-making, experiments mitigate risk by limiting the proportion of customers exposed to innovations. Since many experiments are conducted sequentially over time, an emerging strategy to further derisk the process is to allow managers to ``peek'' at the results as new data become available and stop the test if the results…

随时有效的线性模型推断与随机实验中的回归调整因果推断AI 解读

Linear models are foundational tools in statistics and ubiquitous across the applied sciences. However, conventional statistical inference -- such as $t$-tests and $F$-tests -- are only valid at fixed sample sizes, making them unsuitable for sequential settings such as online A/B testing. We develop an anytime-valid theory of inference for the linear model, introducing sequential analogues of classical tests and confidence sets that provide Type-I error control and coverage…

实验项目收益的优化AI 解读

Experimentation in online digital platforms is used to inform decision making. Specifically, the goal of many experiments is to optimize a metric of interest. Null hypothesis statistical testing can be ill-suited to this task, as it is indifferent to the magnitude of effect sizes and opportunity costs. Given access to a pool of related past experiments, we discuss how experimentation practice should change when the goal is optimization. We survey the literature on empirical…