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

• URL: http://arxiv.org/abs/2606.21212v1
• Date: Fri, 19 Jun 2026 08:30:38 GMT
• Title: DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
• Abstract summary: Causal discovery is critical for understanding complex data-generating mechanisms.<n>Recent foundation models based on Prior-Data Fitted Networks (PFNs) have demonstrated remarkable zero-shot inference capabilities.<n>We propose DCD-PFN, a decoupling-aware foundation model for causal discovery.
• Score: 43.33126904440643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer from severe 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. Instead of directly amortizing global graph reconstruction, DCD-PFN focuses on local causal discovery through a decoupling-based paradigm. Through pre-training on diverse synthetic Structural Causal Models (SCMs), the model learns sample-wise decoupling weights that enable Markov boundary (MB) identification. Furthermore, by leveraging parallelized local discovery, DCD-PFN efficiently reconstructs global causal graphs while remaining grounded in the theoretical foundations of decoupling-based causal discovery. Experiments demonstrate that our foundation model achieves robust zero-shot generalization.

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