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

@article{FernandezLoria2020ACO,
  title={A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation},
  author={Carlos Fern'andez-Lor'ia and Foster J. Provost and J. Anderton and Benjamin Carterette and Praveen Chandar},
  journal={Information Systems Research},
  year={2020}
}
This study presents a systematic comparison of methods for individual treatment assignment. We group the various methods proposed in the literature into three general classes of algorithms (or metalearners): learning models to predict 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 discuss how the metalearners differ in their level of generality and their objective function… 

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