Rationale for causal inference and Bayesian modeling

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The reason for taking a causal inference perspective is straightforward and compelling. All of the quantities that MMM estimates imply causality. ROI, response curves, and optimal budget analysis pertain to how marketing spending affects KPIs, by considering what would have happened if the marketing spend had been different. The Meridian design perspective is that there is no alternative but to use causal inference methodology.

Meridian is a regression model. The fact that marketing effects can be interpreted as causal is owed to the estimands defined and the assumptions made (such as the causal DAG). Although these assumptions might not hold for every advertiser, the assumptions are transparently disclosed for each advertiser to decide.

Although Bayesian modeling is not necessary for causal inference, Meridian takes a Bayesian approach because it offers the following advantages:

1. The prior distributions of a Bayesian model offer an intuitive way to regularize the fit of each parameter according to prior knowledge and the selected regularization strength. Regularization is necessary in MMM because the number of variables is large, the correlations are often high, and the media effects (with adstock and diminishing returns) are complex.
2. Meridian offers the option to reparameterize the regression model in terms of ROI, allowing the use of any custom ROI prior. Any and all available knowledge, including experiment results, can be used to set priors that regularize towards results you believe in with the strength you believe is appropriate.
3. Media variable transformations (adstock and diminishing returns) are nonlinear, and the parameters of these transformations cannot be estimated by linear mixed model techniques. Meridian uses state-of-the-art MCMC sampling techniques to solve this problem.