CAUSAL VALIDATION OF MULTIVARIATE REGRESSION MODELS

Abstract

To evaluate the causal generalizability of multivariate regression models (such as marketing mix models) that evaluate a plurality of input features that may have high correlation and confounding causality, a model architecture is evaluated with respect to experimental data that varies feature values. The model architecture is trained with training data that excludes the experimental data. The trained model is then applied to predict the outcome of the experimental data inputs and the predicted outcome is scored with respect to the experimental outcome. This may be repeated across more than one experiment to evaluate how the model architecture generalizes to different types of variations in different experiments. The scores may then be used to validate the causal predictions and select or confirm a model architecture for use.

CPC Classifications