The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction

ArXi:2605.29411v1 Announce Type: cross Under standard graphical assumptions, the Marko boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set.