Learning Causal Orderings for In-Context Tabular Prediction

ArXi:2605.22335v1 Announce Type: new In-context learning for tabular data sets strong predictive standards in observational settings; it. however. primarily relies on correlational structure, which becomes unreliable under distribution shift or intervention. While established methods to discover causal structure exist, they are often focused on structure identifiability and decoupled from the predictive architectures that could benefit from them.