CSALT: Training-Free Causal Skeleton Learning with Frozen Tabular Foundation Models

Learning causal skeletons from observational data is a fundamental yet challenging task in causal discovery. Constraint-based methods may lose power when data are scarce; continuous-optimization methods require per-dataset refitting; and pretrained causal discovery models require large-scale pretraining on simulations labeled with true graphs. Tabular foundation models offer another source of structural information for causal skeletons, but their attention gives only relative rankings and no statistical rule for determining edge existence. To bridge this gap, we introduce CSALT (Causal Skeleton Learning via Attention-Led Testing), which turns attention from a frozen tabular foundation model into the weights of a target-wise weighted Benjamini-Hochberg procedure. Permuting the target column on a disjoint split gives each candidate a permutation score, and an OR rule combines the target-wise rejection sets into an undirected skeleton. CSALT keeps all model parameters frozen and does not require additional pretraining with ground-truth graph supervision. We theoretically derive a sufficient condition involving attention ranking margins for excluding false edges with finitely many permutations, and further conditions for exact skeleton recovery. Empirical results on nonlinear synthetic data show that CSALT attains higher skeleton precision and F1 than all evaluated baselines. On two real datasets, our method achieves the highest F1 on UF and ties for the highest F1 on Sachs.