# Evaluation of Active Feature Acquisition Methods for Static Feature Settings

arXiv: https://arxiv.org/abs/2312.03619 · HTML: https://mlsanity.com/paper/2312.03619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 19 of 19)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE](https://mlsanity.com/paper/1809.11142/) (2018)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [Active Feature Acquisition with Generative Surrogate Models](https://mlsanity.com/paper/2010.02433/) (2020)
- [Full Law Identification In Graphical Models Of Missing Data: Completeness Results](https://mlsanity.com/paper/2004.04872/) (2020)
- [Classification with Costly Features as a Sequential Decision-Making Problem](https://mlsanity.com/paper/1909.02564/) (2019)
- [Reinforcement Learning with Efficient Active Feature Acquisition](https://mlsanity.com/paper/2011.00825/) (2020)
- [Identification In Missing Data Models Represented By Directed Acyclic Graphs](https://mlsanity.com/paper/1907.00241/) (2019)
- [ASAC: Active Sensing using Actor-Critic models](https://mlsanity.com/paper/1906.06796/) (2019)
- [Inverse Active Sensing: Modeling and Understanding Timely Decision-Making](https://mlsanity.com/paper/2006.14141/) (2020)
- [Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings](https://mlsanity.com/paper/2312.01530/) (2023)
- [Semiparametric theory](https://mlsanity.com/paper/1709.06418/) (2017)
- [An Optimal Policy for Patient Laboratory Tests in Intensive Care Units](https://mlsanity.com/paper/1808.04679/) (2018)
- [Dynamic Measurement Scheduling for Event Forecasting using Deep RL](https://mlsanity.com/paper/1901.09699/) (2019)
- [Dynamic Feature Acquisition with Arbitrary Conditional Flows](https://mlsanity.com/paper/2006.07701/) (2020)
- [Efficient estimation of optimal regimes under a no direct effect assumption](https://mlsanity.com/paper/1908.10448/) (2019)
- [Pattern graphs: a graphical approach to nonmonotone missing data](https://mlsanity.com/paper/2004.00744/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Learning-To-Measure: In-Context Active Feature Acquisition](https://mlsanity.com/paper/2510.12624/) (2025)
- [Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling](https://mlsanity.com/paper/2602.16979/) (2026)
- [Active Feature Acquisition With Incomplete Training Data](https://mlsanity.com/paper/2609.32325/) (2026)

## Abstract (arXiv)

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.