  Active feature acquisition (AFA) agents, crucial in domains like healthcare
where acquiring features is often costly or harmful, determine the optimal set
of features for a subsequent classification task. As deploying an AFA agent
introduces a shift in missingness distribution, it's vital to assess its
expected performance at deployment using retrospective data. In a companion
paper, we introduce a semi-offline reinforcement learning (RL) framework for
active feature acquisition performance evaluation (AFAPE) where features are
assumed to be time-dependent. Here, we study and extend the AFAPE problem to
cover static feature settings, where features are time-invariant, and hence
provide more flexibility to the AFA agents in deciding the order of the
acquisitions. In this static feature setting, we derive and adapt new inverse
probability weighting (IPW), direct method (DM), and double reinforcement
learning (DRL) estimators within the semi-offline RL framework. These
estimators can be applied when the missingness in the retrospective dataset
follows a missing-at-random (MAR) pattern. They also can be applied to
missing-not-at-random (MNAR) patterns in conjunction with appropriate existing
missing data techniques. We illustrate the improved data efficiency offered by
the semi-offline RL estimators in synthetic and real-world data experiments
under synthetic MAR and MNAR missingness.
