A Bias Correction Approach for Interference in Ranking Experiments
提出一种偏差校正方法，能从存在干扰问题的历史A/B测试中恢复排名算法的总平均处理效应，对需要评估排名算法效果的学者和工程师有用。
A bias-correction approach that can recover the total average treatment effect of a ranking algorithm based on past A/B tests even if those tests suffer from interference issues.