DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery

Causal discovery from observational data is critical for understanding complex data-generating mechanisms, yet traditional methods often struggle under strong nonlinearity or noise, and may suffer from computational bottlenecks. Recent tabular foundation models based on Prior-Data Fitted Networks (PFNs) have demonstrated remarkable zero-shot inference capabilities, but their potential for explicit structural causal discovery remains underexplored. To bridge this gap, we propose DCD-PFN, a decoupling-aware foundation model for causal discovery. Rather than directly learning a mapping from data to global graphs, DCD-PFN amortizes decoupling-based local causal discovery. Through pre-training on diverse synthetic Structural Causal Models (SCMs), the model learns to infer sample-wise decoupling weights from observational data, which guide zero-shot Markov boundary (MB) identification in a single forward pass. DCD-PFN recovers global causal structures via a principled reconstruction procedure with parallelized local discovery. Experiments on both synthetic and real-world datasets demonstrate robust zero-shot generalization.