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arXiv · 2603.20791

Neural Autoregressive Flows for Markov Boundary Learning

Khoa Nguyen·Bao Duong·Viet Huynh·Thin Nguyen
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Abstract

Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances improve upon traditional constraint-based techniques by scoring local causal structures, they still rely on nonparametric estimators and heuristic searches, lacking theoretical guarantees for reliability. This paper investigates a framework for efficient Markov boundary discovery by integrating conditional entropy from information theory as a scoring criterion. We design a novel masked autoregressive network to capture complex dependencies. A parallelizable greedy search strategy in polynomial time is proposed, supported by analytical evidence. We also discuss how initializing a graph with learned Markov boundaries accelerates the convergence of causal discovery. Comprehensive evaluations on real-world and synthetic datasets demonstrate the scalability and superior performance of our method in both Markov boundary discovery and causal discovery tasks.

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Khoa Nguyen, Bao Duong, Viet Huynh, Thin Nguyen. 2026-03-21. Neural Autoregressive Flows for Markov Boundary Learning. https://arxiv.org/abs/2603.20791

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