Causal estimands and estimation
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This section describes how Meridian defines the primary estimands of
interest, including incremental outcome, ROI, marginal ROI, and response
curves. These quantities are defined using potential outcomes and
counterfactuals, which are the language of causal inference.
With clear estimand definitions in place, you can review the assumptions
required for the MMM to provide valid inference. These assumptions help ensure
that the model is actually able to estimate these quantities. If assumptions are
not met, then estimates can be severely biased.
We recommend that you clearly define causal estimands and required assumptions
for any MMM methodology. If this is not done, then the model results are likely
to be misinterpreted. Even more impactful, ignoring the required assumptions can
render the analysis practically nonsensical due to severe underlying bias.
The definitions in the following section don't rely on any aspects of the
Meridian model specification. The same definitions can apply to any MMM.
Defining the causal estimand is crucial for any MMM analysis so that the results
are interpretable, and to help determine whether a particular model is
appropriate for the analysis and under what assumptions.
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Last updated 2024-12-05 UTC.
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