Markov Blanket Discovery

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse
Sep 10, 2026Vinura Galwaduge, Jagath SamarabanduCounterfactual Explanations Cost-Aware Inference
Dept. of Electrical and Computer Engineering Western University London, ON, Canada
Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets
Sep 4, 2026Lei Zan, Charles K. Assaad, Emilie Devijver +1Markov Blanket Discovery Time-Series Causal Discovery
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France · Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F75012, Paris, France
High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption
Jul 29, 2026Loong Kuan Lee, Ragavi Krishnamoorthy, Nico PiatkowskiBayesian Networks Markov Blanket Discovery
Hybrid Intelligence, Fraunhofer IAIS, Sankt Augustin, Germany
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Jun 19, 2026Zhengkang Guan, Yikang Chen, Yi He +5Causal Foundation Models Structural Causal Models
College of Computer Science and Technology Zhejiang University
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
Jun 15, 2026Riccardo Cadei, Frank Otchere, Nyasha Tirivayi +3Causal Effect Estimation Conditional Average Treatment Effect Estimation
ISTA · UNICEF · UCLA
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Jun 4, 2026Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2Markov Blanket Discovery Root Cause Analysis
Computer Science Department TU Darmstadt, Germany
Neural Autoregressive Flows for Markov Boundary Learning
Mar 21, 2026Khoa Nguyen, Bao Duong, Viet Huynh +1Markov Blanket Discovery Causal Structure Learning
Deakin Applied Artificial Intelligence Initiative, Geelong, Australia · Edith Cowan University, Perth, Australia
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
Feb 6, 2024Chenxi Liu, Yongqiang Chen, Tongliang Liu +4Markov Blanket Discovery Causal Discovery with LLMs
TMLR Group, Hong Kong Baptist University, Kowloon, Hong Kong SAR · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA +3

Sep 10, 2026J/K move · Enter open · S save

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

Vinura Galwaduge, Jagath Samarabandu

Dept. of Electrical and Computer Engineering Western University London, ON, Canada

Counterfactual Explanations Cost-Aware Inference Active Learning Active Feature Acquisition Markov Blanket Discovery Algorithmic Recourse

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Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made with partial information, because features are costly to acquire. Active feature acquisition addresses cost-constrained prediction, but existing methods are explanation-agnostic: prior work provides explanations only after acquiring additional features, rather than using explanations to drive acquisition. This work flips that and treats algorithmic recourse and feature acquisition jointly. We use Markov Blanket theory to unify counterfactual, semifactual, and alterfactual explanations and to characterize how available recourse grows as features are acquired. Building on this framework, we propose an Explanation-Driven Feature Acquisition (EDFA) method that selects features by explanatory value per unit cost. The framework is further extended with distribution-free validity guarantees for recourse issued from partial information, which signal trustworthy, lower-cost recourse, along with a lower bound on the calibration data required to certify them. Experiments on 7 publicly available datasets with neural network-based predictive models show that EDFA acquires substantially fewer features than state-of-the-art AFA baselines while maintaining comparable accuracy and yielding more decision-relevant, actionable recourse. The implementation is available on GitHub.