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This study presents a systematic comparison of methods for individualtreatment assignment, a general problem that arises in many applications andhas received significant attention from economists, computer scientists, andsocial scientists. We group the various methods proposed in the literature intothree general classes of algorithms (or metalearners): learning models topredict outcomes (the O-learner), learning models to predict causal effects(the E-learner), and learning models to predict optimal treatment assignments(the A-learner). We compare the metalearners in terms of (1) their level ofgenerality and (2) the objective function they use to learn models from data;we then discuss the implications that these characteristics have for modelingand decision making. Notably, we demonstrate analytically and empirically thatoptimizing for the prediction of outcomes or causal effects is not the same asoptimizing for treatment assignments, suggesting that in general the A-learnershould lead to better treatment assignments than the other metalearners. Wedemonstrate the practical implications of our findings in the context ofchoosing, for each user, the best algorithm for playlist generation in order tooptimize engagement. This is the first comparison of the three differentmetalearners on a real-world application at scale (based on more than half abillion individual treatment assignments). In addition to supporting ouranalytical findings, the results show how large A/B tests can providesubstantial value for learning treatment assignment policies, rather thansimply choosing the variant that performs best on average.

A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation