Beyond Candidate Voting: Transformer Aggregation Meets Observational Evidence

Causal discovery algorithms often return different graphs because they rely on different functional, distributional, and conditional-independence assumptions. Candidate-only ensembles can reduce this instability, but their predictions remain limited to information retained in the submitted graphs. We introduce CausalFusionFormer, a causal discovery framework comprising progressively richer variants: globally learned candidate weighting, edge-adaptive transformer aggregation, an independent data-driven expert, and a full hybrid that routes between transformer-aggregated candidates and observational evidence. Our primary candidate pool contains PC, GES-BIC, ICA-LiNGAM, NOTEARS, and DAGMA. Each algorithm contributes its full-data estimate and two bootstrap perturbations, yielding a shared 15-graph evidence budget. On 90 previously untouched locked tasks, the full hybrid obtains a directed F1 of \(0.661\), compared with \(0.612\) for a shared-budget adaptation of Causal-Bayes-Ensemble (CBE) and \(0.608\) for our global candidate-weighting variant (vo2026wild). Relative to the CBE adaptation, it improves directed F1 by \(0.050\) (\(p=0.0091\)) and AUPRC by \(0.094\) (\(p<10^-4\)). The framework also exposes complementary operating points: uniform candidate voting provides the strongest skeleton recovery and normalized structural Hamming distance, while our global-weighting variant provides the best calibration. On continuous structural-equation data generated over unseen Asia, CHILD, and ALARM graph topologies, the full hybrid achieves the highest macro directed F1. On Sachs, our global candidate-weighting variant ties matched full-data PC at \(0.439\) directed F1, while the full hybrid matches the CBE adaptation. Together, these results show that edge-adaptive transformer aggregation and observational correction can improve directed causal recovery beyond candidate-only Bayesian voting, while complementary variants of the framework support different accuracy, calibration, and transfer requirements.