Read an attribution methodology doc in 15 minutes
Five things to look for, in order, and what a missing one tells you. A structured skim that gets more out of the document than reading it twice.
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By Joris van Huët, Founder & CEOUpdated 3 min read
Fifteen minutes and five targets, in this order. A missing target is more informative than anything present.
The five
| Order | Find | If missing |
|---|---|---|
| 1 | The input specification | You cannot tell what the estimate is built from |
| 2 | The comparison, stated explicitly | There is no counterfactual, so "lift" is decoration |
| 3 | The source of uncertainty | Intervals, if shown, are decorative too |
| 4 | The design classification | You cannot rank this evidence against a test |
| 5 | The stated limits | The document is marketing |
One: the input
Look for exactly what data goes in and at what grain. "Your marketing data" is not a specification. "A channel-level acquisition export by day, plus order counts" is. If the input is vague, everything downstream is unverifiable, because you cannot reproduce something whose starting point is undefined.
Two: the comparison
This is the single most important target. Find the sentence that says what the estimate is measured against. Against a period? Against a held-out group? Against modelled variation in the same data?
If no such sentence exists, the document is describing a credit allocation rather than a causal estimate, which is a different thing and worth knowing before you buy. The distinction is in what multi-touch attribution never measured.
Three: uncertainty
Where does the interval come from, and what makes it wider. A good document says something like: spend variation in the window, the size of the channel, and the length of the period. A document that shows intervals without explaining them is showing a graphic.
Four: design classification
Experimental, quasi-experimental or observational. Most reads are observational and that is fine. A document that will not classify itself is avoiding the ranking, which is precisely the information you wanted.
Five: limits
Find the section where the vendor says what the method cannot do. Its absence is the strongest single signal in this exercise.
What to do with a good document
Design a check from it. If the method says intervals widen when spend is flat, run it on a flat window and see whether they do. That takes an afternoon and it is worth more than any amount of reading, and the approach is in using the methodology doc to design your own check.
Ours, for comparison
Causality Engine reads one file, the Attribution paths export from GA4, and shows what each channel caused next to what last-click gave it.
Related answers
Frequently asked questions
What should I look for first in a methodology document?
The input specification and then the comparison. If the document never says what the estimate is measured against, it is describing a credit allocation rather than a causal estimate, which is a different product than the marketing implies.What does a missing limits section mean?
It is the strongest single signal in the exercise. Every method has boundaries, so a document that names none was written to sell rather than to explain, regardless of how technical the rest of it appears.How do I verify a methodology claim?
Design a check from it. If the document says intervals widen when spend is flat, run the tool on a flat window and see whether they do. An afternoon of that is worth more than reading the document twice.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- Ad SpendAd Spend is the total amount invested in advertising campaigns. It is measured against Return on Ad Spend (ROAS) to evaluate campaign effectiveness.
- 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.
- CounterfactualA counterfactual is what would have happened under conditions that did not occur, such as a customer's outcome had they not received the campaign they did receive. It is never observed, so every effect estimate has to stand in for it with a control group, comparable units or a forecast.
- 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.
- Quasi-ExperimentA quasi-experiment estimates the causal impact of an intervention without random assignment. It applies when random assignment is not feasible or ethical.