论文检索

随机对照试验中保护隐私的分位数处理效应估计AI 解读

In accordance with the principle of "data minimization", many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using…

整群随机实验中 Delta 方法与聚类稳健方差估计的等价性AI 解读

It often happens that the same problem presents itself to different communities and the solutions proposed or adopted by those communities are different. We take the case of the variance estimation of the population average treatment effect in cluster-randomized experiments. The econometrics literature promotes the cluster-robust variance estimator (Athey and Imbens, 2017), which can be dated back to the study of linear regression with clustered residuals (Liang and Zeger,…

新奇效应与先入效应：在线实验的长期效应估计AI 解读

Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always permanently stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this paper, we focus on a particular cause, user-learning, which is primarily associated with…