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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:
- 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.
- 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.
- 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.
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Last updated 2025-06-11 UTC.
[null,null,["Last updated 2025-06-11 UTC."],[[["\u003cp\u003eMeridian adopts a causal inference perspective to measure the true impact of marketing spending on key performance indicators (KPIs) such as ROI, response curves, and optimal budget allocation.\u003c/p\u003e\n"],["\u003cp\u003eBuilt as a Bayesian regression model, Meridian leverages causal assumptions and transparently discloses them, allowing advertisers to assess their applicability.\u003c/p\u003e\n"],["\u003cp\u003eThe Bayesian approach in Meridian provides robust regularization, incorporates prior knowledge about ROI, and effectively handles non-linear media effects through advanced sampling techniques.\u003c/p\u003e\n"]]],["Meridian uses causal inference methodology because MMM estimates imply causality, analyzing how marketing spend affects KPIs. This regression model defines estimands and makes assumptions, which are disclosed for transparency. It employs a Bayesian approach for regularization via prior distributions, reparameterization using ROI priors, and handling nonlinear media variable transformations like adstock and diminishing returns through MCMC sampling techniques. These techniques are needed due to high variable counts and complex media effects.\n"],null,["# Rationale for causal inference and Bayesian modeling\n\nThe reason for taking a causal inference perspective is straightforward and\ncompelling. All of the quantities that MMM estimates imply causality. ROI,\nresponse curves, and optimal budget analysis pertain to how marketing spending\naffects KPIs, by considering what would have happened if the marketing spend had\nbeen different. The Meridian design perspective is that there is no alternative\nbut to use causal inference methodology.\n\nMeridian is a regression model. The fact that marketing effects can be\ninterpreted as causal is owed to the estimands defined and the assumptions made\n(such as the causal DAG). Although these assumptions might not hold for every\nadvertiser, the assumptions are transparently disclosed for each advertiser to\ndecide.\n\nAlthough Bayesian modeling is not necessary for causal inference,\nMeridian takes a Bayesian approach because it offers the following\nadvantages:\n\n1. 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.\n2. 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.\n3. 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\n techniques](/meridian/docs/basics/bayesian-inference#mcmc-convergence) to solve this problem."]]
