Neural Autoregressive Flows for Markov Boundary Learning

ArXi:2603.20791v1 Announce Type: new Recovering Marko 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 Marko boundary discovery by integrating conditional entropy from information theory as a scoring criterion.