Google Meridian | Controls, Mediators and Treatments

Why It Matters

Accurate causal ROI lets marketers allocate spend efficiently, preventing budget waste from biased attribution.

Key Takeaways

• Meridian uses DAGs to isolate causal marketing impacts
• Confounders affect both treatments and KPI; must be controlled
• Predictors improve precision but don’t de-bias causal estimates
• Mediators must be excluded to avoid blocking causal pathways
• Intervenable variables are treatments; non‑intervenable are controls in models

Summary

Google’s Meridian platform relies on Directed Acyclic Graphs to separate causal marketing effects from noise. The model maps treatments, KPI, confounding controls, predictor controls and mediators across time periods, allowing lagged impacts and ensuring that only true causal pathways are estimated.

Key insights include the need to feed major confounders—variables influencing both spend and outcomes—into the model, while strong predictors improve estimate precision without removing bias. Mediators such as website visits must be excluded, as holding them constant would block the ad’s effect. Variables are classified by intervenability: non‑intervenable factors like weather become controls, whereas price changes are treated as non‑media treatments.

The video illustrates this with a competitor’s promotion acting as a confounder, website visits as a mediator, and weather as a non‑intervenable control. It also highlights baseline challenges: TV ads have a clear zero‑impression baseline, but weather lacks a meaningful reference, making contribution estimates unreliable.

Correctly sorting variables ensures Meridian delivers genuine causal ROI rather than a simple fit, enabling marketers to allocate budgets confidently and avoid costly mis‑attribution.

Original Description

Explore the causal logic driving Meridian's insights. This video unpacks the Directed Acyclic Graph (DAG) framework, detailing how to correctly classify variables into treatments (paid, organic, and non-media), confounding controls, and predictor controls—and why you must strictly avoid mediators to ensure unbiased causal estimates.

Learn more:

https://developers.google.com/meridian/docs/advanced-modeling/control-variables

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