What an AI agent needs from attribution data
An agent inherits whatever certainty your data implies. Four fields that stop a model turning an unresolved estimate into a confident recommendation.
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By Joris van Huët, Founder & CEOUpdated 2 min read
An agent inherits whatever certainty your data implies, and a bare number implies total certainty.
Why this matters more with agents than with people
A person reading a dashboard applies scepticism from experience: they know branded search tends to look better than it is, they remember the window was odd. An agent has none of that context and will treat the numbers as given.
So the uncertainty has to be in the data rather than in the reader. That is a general principle for any machine-consumed measurement, and it is the reason a bare CSV of ROAS figures is a worse input than it looks.
The format questions that matter less than people think
JSON against CSV, field naming, endpoint shape. All solvable and all secondary. A well-named schema carrying only point estimates is worse than an awkward one carrying intervals.
What we expose
Causality Engine returns per channel: what it caused next to what last-click gave it, a data-health score rather than a confidence interval, and a next step, with receipts that check every number against the export. The agent-facing surface is described on for AI assistants.
The limit worth encoding
An agent can read the estimate and its uncertainty and draft a recommendation.
The standard, in one line
If your data cannot express "we do not know", any system reading it will conclude that you do. That applies to dashboards, to reports, and most sharply to agents.
Related answers
Frequently asked questions
Why do agents need uncertainty more than people do?
Because a person applies scepticism from experience, knowing which channels tend to look better than they are. An agent has no such context and treats the numbers as given, so the uncertainty has to live in the data.Does Causality Engine let an agent change budgets?
No. An agent can read the estimate and its uncertainty and draft a recommendation.
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.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ClickClick is the action a user takes to interact with a digital advertisement, redirecting them to a website or landing page. Clicks are a fundamental metric for measuring ad engagement and a primary input for click-based attribution models.
- Confidence IntervalConfidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
- DashboardA dashboard is a visual display of key information required to achieve specific objectives. It consolidates data onto a single screen for quick review.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- ShopifyShopify is an ecommerce platform for creating online stores and selling products. Attribution modeling shows which marketing channels drive traffic and conversions within Shopify.