Hierarchical clustering of geos by marketing spend correlation after normalization reduces multicollinearity and enables separate causal identification of ad channel effects in a Bayesian marketing mix model.
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Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference
Hierarchical clustering of geos by marketing spend correlation after normalization reduces multicollinearity and enables separate causal identification of ad channel effects in a Bayesian marketing mix model.