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Active feature acquisition (AFA) agents, crucial in domains like healthcarewhere acquiring features is often costly or harmful, determine the optimal setof features for a subsequent classification task. As deploying an AFA agentintroduces a shift in missingness distribution, it's vital to assess itsexpected performance at deployment using retrospective data. In a companionpaper, we introduce a semi-offline reinforcement learning (RL) framework foractive feature acquisition performance evaluation (AFAPE) where features areassumed to be time-dependent. Here, we study and extend the AFAPE problem tocover static feature settings, where features are time-invariant, and henceprovide more flexibility to the AFA agents in deciding the order of theacquisitions. In this static feature setting, we derive and adapt new inverseprobability weighting (IPW), direct method (DM), and double reinforcementlearning (DRL) estimators within the semi-offline RL framework. Theseestimators can be applied when the missingness in the retrospective datasetfollows a missing-at-random (MAR) pattern. They also can be applied tomissing-not-at-random (MNAR) patterns in conjunction with appropriate existingmissing data techniques. We illustrate the improved data efficiency offered bythe semi-offline RL estimators in synthetic and real-world data experimentsunder synthetic MAR and MNAR missingness.

Evaluation of Active Feature Acquisition Methods for Static Feature Settings