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This paper proposes a novel approach for constructing effective personalizedpolicies when the observed data lacks counter-factual information, is biasedand possesses many features. The approach is applicable in a wide variety ofsettings from healthcare to advertising to education to finance. These settingshave in common that the decision maker can observe, for each previous instance,an array of features of the instance, the action taken in that instance, andthe reward realized -- but not the rewards of actions that were not taken: thecounterfactual information. Learning in such settings is made even moredifficult because the observed data is typically biased by the existing policy(that generated the data) and because the array of features that might affectthe reward in a particular instance -- and hence should be taken into accountin deciding on an action in each particular instance -- is often vast. Theapproach presented here estimates propensity scores for the observed data,infers counterfactuals, identifies a (relatively small) number of features thatare (most) relevant for each possible action and instance, and prescribes apolicy to be followed. Comparison of the proposed algorithm against thestate-of-art algorithm on actual datasets demonstrates that the proposedalgorithm achieves a significant improvement in performance.

Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features