What an attribution API returns that dashboards hide: Dashboards are edited. The API response is not. Four fields that tend to exist in the payload and never make it onto the screen, and what each one tells you.
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The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
A dashboard is an edited view, and the edit usually removes the fields that would have made you cautious. Reading the raw response is the cheapest way to find out what a tool knows and does not show.
This is not an accusation of bad faith. Interfaces are designed to be legible, and legibility means dropping columns. The problem is that the columns dropped first are almost always the ones that qualify the headline.
Four fields worth looking for
| Field | Typical dashboard treatment | What it tells you |
|---|---|---|
| Interval width | Hidden, or shown as a faint band | Whether the estimate can support a decision |
| Coverage | Absent | What share of real orders the read explains |
| Design label | Absent | How this evidence ranks against a test |
| Declined channels | Silently dropped or imputed | Which channels the method could not measure |
The fourth row is the interesting one. When a channel has too little spend for its effect to be separated from noise, a well-built method declines to score it. A dashboard that shows a tidy ranked list of every channel has either measured them all or quietly filled the gaps. Those are very different products and the interface looks identical.
How to check without building anything
You do not need an integration to find this out. Ask the vendor for a sample response payload for one account and one window. A vendor confident in its method will send one. Compare the fields in the payload against the fields on the screen, and ask about anything present in one and absent from the other.
We think this is a reasonable thing to ask of anyone, including us, and set out the wider case in why attribution vendors should explain methodology openly. The buying-side version is the checklist for vetting an attribution vendor.
The interval is the field that changes behaviour
Of the four, interval width is the one that most often changes what a team does. A channel with an estimate of 1.8 and an interval from 1.6 to 2.0 supports a decision. The same 1.8 with an interval from 0.4 to 3.2 does not, and the dashboard renders both as 1.8.
The practical consequence is that teams scale channels on estimates that were never resolved, then conclude attribution does not work when the scaling does not pay. It worked fine. The width was hidden. Attribution tools that report confidence intervals covers who publishes them.
Coverage is the field that changes the framing
Coverage answers a question nobody thinks to ask: of the orders you actually took in this window, what share does this read explain? If it is high, the channel estimates describe your business. If it is low, they describe a slice of it, and the rest is sitting in unassigned traffic doing something the model cannot see. That is a normal condition and it is worth knowing before you reallocate.
What we return
Causality Engine returns the estimate, the interval, the coverage and the design label per channel, and names the channels below the measurable floor rather than scoring them. The output is visible without a signup on the interactive demo, and the method is written out on how it works. Developer API keys and the MCP server sit on Pro at €299 a month; the €99 one-time read produces the same fields from a manual upload.
The question to carry into any demo
Ask what the model does with a channel it cannot measure. The answer separates tools that are honest about their floor from tools that have no floor at all, and it takes about ten seconds to ask.
Related answers
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Key Terms in This Article
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Attribution Report
Attribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Confidence Interval
Confidence 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.
Dashboard
A dashboard is a visual display of key information required to achieve specific objectives. It consolidates data onto a single screen for quick review.
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Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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