The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
The episode critiques 'The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction,' arguing that feature selection should prioritize predictive performance over strict structural causality. Hosts discuss moving from deductive theoretical purity to inductive reasoning based on empirical gain, suggesting new methods for building robust AI systems.