USPTO published patent application US20260094172A1 for a causal validation method for multivariate regression models. The invention addresses evaluation of causal generalizability in regression models (such as marketing mix models) that assess multiple input features with high correlation and confounding causality. The method involves training a model architecture using training data excluding experimental data, applying the trained model to predict outcomes from experimental inputs, and scoring predictions against experimental outcomes. This process may be repeated across multiple experiments to evaluate how the model architecture generalizes to different variations.

This is a patent publication rather than a regulatory requirement, so no compliance actions are required from companies. Technology companies developing multivariate regression models, particularly marketing mix models, may wish to review this methodology for potential licensing considerations or competitive awareness. The patent covers CPC classification G06Q 30/0201 (business methods) and does not impose any regulatory obligations.