Multi-collinearity occurs when two or more independent variables in a regression model are highly correlated. This complicates the model's ability to discern individual variable effects.
In marketing mix modelling, multi-collinearity is often problematic because overlapping marketing channels, like TV and social media, can obscure the effectiveness of individual contributions.
Detecting and addressing multi-collinearity is crucial to ensure accurate causal inference. Employing Bayesian priors can provide a way to alleviate this issue in regression models.
Bayesian methods and random budget adjustments can help resolve multi-collinearity by enhancing the model's ability to distinguish the individual effects of correlated variables.
Collection
[
|
...
]