Join the newly launched
Discord community for real-time discussions, peer support, and direct interaction with the Meridian team!
Causal estimands and estimation
Stay organized with collections
Save and categorize content based on your preferences.
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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-06-11 UTC.
[null,null,["Last updated 2025-06-11 UTC."],[[["\u003cp\u003eThis section outlines how to define primary estimands of interest in Marketing Mix Modeling (MMM), such as incremental outcome, ROI, marginal ROI, and response curves, using the framework of potential outcomes and counterfactuals.\u003c/p\u003e\n"],["\u003cp\u003eClear estimand definitions enable the evaluation of assumptions necessary for valid inference in MMM, ensuring accurate estimations and avoiding biases.\u003c/p\u003e\n"],["\u003cp\u003eDefining causal estimands and assumptions is crucial for any MMM methodology to ensure interpretable results and prevent misinterpretations or biased analysis.\u003c/p\u003e\n"],["\u003cp\u003eThese estimand definitions are universally applicable to any MMM, regardless of the specific model used, emphasizing the importance of defining them for interpretability and model appropriateness assessment.\u003c/p\u003e\n"]]],["Meridian defines primary estimands like incremental outcome, ROI, marginal ROI, and response curves using potential outcomes and counterfactuals. Defining these estimands and the assumptions needed for valid inference is crucial for any Marketing Mix Model (MMM). Failure to meet these assumptions can severely bias estimates, making the analysis unreliable. These definitions are applicable to any MMM, aiding in result interpretation and assessing model appropriateness.\n"],null,["# Causal estimands and estimation\n\nThis section describes how Meridian defines the primary estimands of\ninterest, including incremental outcome, ROI, marginal ROI, and response\ncurves. These quantities are defined using potential outcomes and\ncounterfactuals, which are the language of causal inference.\n\nWith clear estimand definitions in place, you can review the assumptions\nrequired for the MMM to provide valid inference. These assumptions help ensure\nthat the model is actually able to estimate these quantities. If assumptions are\nnot met, then estimates can be severely biased.\n\nWe recommend that you clearly define causal estimands and required assumptions\nfor any MMM methodology. If this is not done, then the model results are likely\nto be misinterpreted. Even more impactful, ignoring the required assumptions can\nrender the analysis practically nonsensical due to severe underlying bias.\n\nThe definitions in the following section don't rely on any aspects of the\nMeridian model specification. The same definitions can apply to any MMM.\nDefining the causal estimand is crucial for any MMM analysis so that the results\nare interpretable, and to help determine whether a particular model is\nappropriate for the analysis and under what assumptions."]]
