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We study the problem of learning to choose from m discrete treatment options(e.g., news item or medical drug) the one with best causal effect for aparticular instance (e.g., user or patient) where the training data consists ofpassive observations of covariates, treatment, and the outcome of thetreatment. The standard approach to this problem is regress and compare: splitthe training data by treatment, fit a regression model in each split, and, fora new instance, predict all m outcomes and pick the best. By reformulating theproblem as a single learning task rather than m separate ones, we propose a newapproach based on recursively partitioning the data into regimes wheredifferent treatments are optimal. We extend this approach to an optimalpartitioning approach that finds a globally optimal partition, achieving acompact, interpretable, and impactful personalization model. We develop newtools for validating and evaluating personalization models on observationaldata and use these to demonstrate the power of our novel approaches in apersonalized medicine and a job training application.

Recursive Partitioning for Personalization using Observational Data