Researchers investigated the effectiveness of Markov boundary in tabular prediction tasks. They found that restricting a model to the Markov boundary can improve prediction substantially, especially in larger and sparser feature spaces. However, existing methods for recovering the boundary are computationally expensive and often do not deliver significant improvements. The study highlights the importance of considering the asymmetric predictive cost of false positives and negatives when selecting features. --- Why it matters: This research matters to engineers and researchers working on tabular prediction tasks because it provides insights into the limitations of current methods for using Markov boundary in model selection. It also suggests that a more nuanced approach is needed, taking into account the specific characteristics of the feature space and the predictive costs associated with different types of errors. Source: https://arxiv.org/abs/2605.29411