This article explores how multi-collinearity can damage causal inferences in marketing mix modeling and provides methods to address it, including Bayesian priors and random budget adjustments.
This article explains how adding monotonic constraints to traditional ML models can make them more reliable for causal inference, illustrated with a real estate example.
