The Correlation vs Causation Problem in Marketing Attribution
The Correlation vs Causation Problem in Marketing Attribution. Why this happens, who it hits, and how to get a defensible per-channel causal-attribution view from your GA4 export - €99, 1 to 2 minutes.
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By Joris van Huët, Founder & CEOUpdated 2 min read
The Correlation vs Causation Problem in Marketing Attribution
Why none of these dashboards agree
Multi-touch attribution measures which touchpoints were present in a journey, not which one caused the conversion. Two tools applying different MTA rules to the same data will return different credit splits. Neither is closer to the truth.
Why no MTA tool can fix this
The fundamental issue is category. MTA tools answer the question "which touchpoints touched the journey?" with a rule. Causal inference answers a different question: "would this revenue have happened without this channel?" The two are not the same.
FAQ
Will the causal view agree with GA4 last-click? Sometimes yes, sometimes no. Channels that sit mid-funnel (newsletter, organic social, certain influencers) are systematically under-credited by last-click.
Can I just upload my GA4 export? Yes. That is the entire workflow. GA4 only, today. Shopify integration is on the roadmap, not required.
You already have your data. It is sitting in your GA4 property right now.
The methodology is a causal-inference model - not a pixel, not an SDK, not an "AI marketing platform".
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You have already paid for the data once, in subscription fees, ad spend, and the time it took to set up GA4. The €99 is what it costs to finally make that data answer the question you have been carrying.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- InfluencerAn Influencer affects purchase decisions due to their authority, knowledge, or relationship with their audience. They drive consumer behavior.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a complete view of channel impact on conversions.