Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
This addresses the challenge of decision-making in domains like healthcare and advertising where data is biased and high-dimensional, though it appears incremental as it builds on existing counterfactual inference methods.
The paper tackles the problem of learning personalized policies from biased, high-dimensional observational data lacking counterfactual information, and demonstrates that the proposed algorithm achieves significant performance improvements over state-of-the-art methods on actual datasets.
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features. The approach is applicable in a wide variety of settings from healthcare to advertising to education to finance. These settings have 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, and the reward realized -- but not the rewards of actions that were not taken: the counterfactual information. Learning in such settings is made even more difficult because the observed data is typically biased by the existing policy (that generated the data) and because the array of features that might affect the reward in a particular instance -- and hence should be taken into account in deciding on an action in each particular instance -- is often vast. The approach presented here estimates propensity scores for the observed data, infers counterfactuals, identifies a (relatively small) number of features that are (most) relevant for each possible action and instance, and prescribes a policy to be followed. Comparison of the proposed algorithm against the state-of-art algorithm on actual datasets demonstrates that the proposed algorithm achieves a significant improvement in performance.