Neural Autoregressive Flows for Markov Boundary Learning

• URL: http://arxiv.org/abs/2603.20791v1
• Date: Sat, 21 Mar 2026 12:31:21 GMT
• Title: Neural Autoregressive Flows for Markov Boundary Learning
• Abstract summary: This paper investigates a framework for efficient boundary discovery by integrating conditional entropy from information theory as a scoring criterion.<n>A parallelizable greedy search strategy in time is proposed, supported by analytical evidence.<n> Comprehensive evaluations on real-world and synthetic datasets demonstrate the scalability and superior performance of our method in both boundary discovery and causal discovery tasks.
• Score: 12.567367940875073
• License: http://creativecommons.org/licenses/by/4.0/
• 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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