Defending a finding starts with naming the method
A finding is only as defensible as the design behind it. Naming the design first is what turns a claim into something a sceptic can actually weigh.
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By Joris van Huët, Founder & CEOUpdated 3 min read
A finding is only as defensible as the design that produced it, so name the design before the number. That single ordering change does more for credibility than any amount of presentation polish.
The three designs, and what each earns
| Design | What it does | How much weight it carries |
|---|---|---|
| Randomised holdout | Creates a comparison group deliberately | The most |
| Quasi-experimental | Exploits an accident that behaved like one | Substantial, if the accident is clean |
| Observational | Uses variation already present in the data | Real, and the least of the three |
A sceptic is not asking you to prove certainty. They are asking where on this ladder your claim sits, and a claim that places itself honestly is far harder to dismiss than one that implies more.
Why leading with the number backfires
Leading with the number invites the listener to evaluate the number, which they cannot do, so they evaluate you instead. Leading with the design gives them something they can assess on its merits, and it signals that you know the difference between the rungs.
It also pre-empts the most common objection, which is not "that number is wrong" but "how would you know". Answering before it is asked changes the tenor of the whole discussion.
The three fields that travel with the design
Estimate, confidence interval, coverage. The interval says how much the data could resolve. Coverage says what share of your real orders the estimate explains.
What to concede up front
That an observational read is not a test. That the intervals on your smallest channels are wide. That some channels could not be measured at all. Conceding these first costs nothing, because a sceptic will find them anyway, and finding them yourself is what establishes that the rest is straight.
Where our own output sits
The plain-language method is on how it works.
The sentence to open with
Something close to: this is an observational estimate, here is the range, here is the share of orders it explains, and here is what would change it.
Related answers
Frequently asked questions
How do I make an attribution finding credible?
Name the design before the number. State whether the estimate came from a randomised holdout, a quasi-experiment, or observed variation, then give the interval and the coverage. A claim that places itself honestly is much harder to dismiss.What is the most common objection to attribution findings?
Not that the number is wrong, but how you would know. Answering that before it is asked, by naming the design and its limits, changes the discussion from a defence into an evaluation of evidence.Should I admit the weaknesses in my own analysis?
Yes, and first. A sceptic will find the wide intervals and the unmeasurable channels anyway.
Go deeper: Causal attribution, 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.
- Attribution DebtAttribution debt is the gap between what your ad platforms claim drove revenue and what actually caused it, carried quarter after quarter into the budget. It is how marketing debt accrues: allocate on claimed conversions long enough and the plan itself becomes the liability.
- 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.
- 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.
- Quasi-ExperimentA quasi-experiment estimates the causal impact of an intervention without random assignment. It applies when random assignment is not feasible or ethical.