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.

在这篇研究论文中，我们介绍了一种半离线强化学习框架，用于评估部署活动特征获取性能 (active feature acquisition performance evaluation, AFAPE)。首先，我们将 AFAPE 问题扩展到静态特征环境中，从而为 AFA 代理提供决策特征获取顺序的更大灵活性。然后，在半离线强化学习框架中，我们推导和适应了新的逆概率权重估计器 (inverse probability weighting, IPW)，直接方法估计器 (direct method, DM) 和双重强化学习估计器 (double reinforcement learning, DRL)。这些估计器可应用于具有缺失数据的回顾数据集，无论缺失性是否遵循随机缺失 (missing-at-random, MAR) 模式或非随机缺失 (missing-not-at-random, MNAR) 模式。在合成数据和真实世界数据的实验中，我们展示了半离线强化学习估计器在合成 MAR 和 MNAR 缺失数据下的改进数据效率。

静态特征设置的主动特征获取方法评估