Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
结合多个实验与多个结局高效估计异质性处理效应
Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outcome metric. However, in many real world domains, experiments are run consistently - for example, in internet companies, A/B tests are run every day to measure the impacts of potential changes across many different metrics of interest. We show that even if an analyst cares only about the HTEs in one…