Causal-CAD: Learning Causal Models for Root-Cause Analysis of Parametric CAD Errors

Parametric computer-aided design (CAD) stores both an ordered feature history and a graph of the references consumed by each feature. A local edit can propagate through this dependency graph and prevent distant downstream features from regenerating, while the CAD system often reports only where regeneration stopped. We present a system that learns executable causal models from Autodesk Fusion 360 dependency graphs before and after edits. Each model is a program defining a causal graph and structural functions over its variables. An LLM-guided quality-diversity search evolves these programs for several error families, and a live Fusion agent tests their explanations. Causal explanations from our system achieve a mean score of 0.238 on our repair task test bed, compared with 0.150 for an LLM-only explanation and 0.123 without an explanation. Performance also rises after validating the models in the live Fusion environment, demonstrating the value of integrating learning and acting in a real-world CAD setting.