Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

TL;DR

This paper addresses the evaluation of active feature acquisition strategies in cost-sensitive, time-varying settings, proposing new methods and frameworks for safe deployment and performance assessment.

Contribution

It introduces a novel semi-offline reinforcement learning framework for AFAPE, with three new estimators, under weaker assumptions than previous methods.

Findings

Application of missing data methods under NDE assumption

Use of offline reinforcement learning under NUC assumption

Development of three estimators: DM, IPW, and DRL

Abstract

Machine learning methods often assume that input features are available at no cost. However, in domains like healthcare, where acquiring features could be expensive or harmful, it is necessary to balance a feature's acquisition cost against its predictive value. The task of training an AI agent to decide which features to acquire is called active feature acquisition (AFA). By deploying an AFA agent, we effectively alter the acquisition strategy and trigger a distribution shift. To safely deploy AFA agents under this distribution shift, we present the problem of active feature acquisition performance evaluation (AFAPE). We examine AFAPE under i) a no direct effect (NDE) assumption, stating that acquisitions do not affect the underlying feature values; and ii) a no unobserved confounding (NUC) assumption, stating that retrospective feature acquisition decisions were only based on observed features. We show that one can apply missing data methods under the NDE assumption and offline reinforcement learning under the NUC assumption. When NUC and NDE hold, we propose a novel semi-offline reinforcement learning framework. This framework requires a weaker positivity assumption and introduces three new estimators: A direct method (DM), an inverse probability weighting (IPW), and a double reinforcement learning (DRL) estimator.