Advanced Marketing Mix Modeling with Causal Inference

Construct a comprehensive Python-based marketing mix modeling solution that goes beyond traditional regression by incorporating causal inference techniques. Utilize DoWhy and EconML libraries to estimate true marketing channel effectiveness, accounting for confounding variables and selection bias. Implement bootstrapping for uncertainty estimation, develop counterfactual scenario modeling, and create an interactive dashboard showing estimated causal impacts of marketing investments.