The Causal Calibration System: Stress-Testing Attribution Models via Synthetic Ground Truth

In marketing science, the 'correct' attribution is unknown, creating a validation crisis. This paper presents the Causal Calibration System, a comprehensive testing framework that generates synthetic datasets with known ground-truth causal effects. We subject attribution models to five rigorous stress tests: Last-Touch Bias, Correlated Channels, Interaction Effects, Delayed Effects, and Confounding Variables. The system quantifies error rates, provides calibration scores (0-100), and identifies specific failure modes. This work provides validation methodology for attribution frameworks including Robinson (2026a, 2026b, 2026c).