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Learning heterogeneous treatment effects (HTEs) is an important problemacross many fields. Most existing methods consider the setting with a singletreatment arm and a single outcome metric. However, in many real world domains,experiments are run consistently - for example, in internet companies, A/Btests are run every day to measure the impacts of potential changes across manydifferent metrics of interest. We show that even if an analyst cares only aboutthe HTEs in one experiment for one metric, precision can be improved greatly byanalyzing all of the data together to take advantage of cross-experiment andcross-outcome metric correlations. We formalize this idea in a tensorfactorization framework and propose a simple and scalable model which we referto as the low rank or LR-learner. Experiments in both synthetic and real datasuggest that the LR-learner can be much more precise than independent HTEestimation.

Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes