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A map, organized by the problem you actually have
Measurement problems rarely arrive labeled with their method. They arrive as two dashboards disagreeing, or a campaign that tested well and did nothing. This page routes from the symptom to the part of the curriculum that covers it.
Find the symptom that sounds most like your week
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“Two systems report different numbers for the same campaign.”
Almost always a definition or attribution-window difference rather than a bug. Platforms count conversions they can claim, in windows they choose, against identities they can resolve. Reconcile the definitions before the numbers.
Start with attribution. → -
“Our ROAS looks great but revenue is flat.”
The signature of measuring credit rather than causation. Reported ROAS can rise while incremental revenue falls, because the channels that harvest existing demand are the ones best positioned to claim it.
Read Attribution Is Not Incrementality. → -
“We want to know if a channel is worth the spend.”
This is an incrementality question, so it needs a holdout. The design work matters more than the estimator: what you can withhold, from whom, and for how long.
Work through the geo holdout example. → -
“Our model has great AUC but the campaign did not work.”
You targeted propensity when you needed persuadability. A model that finds who is likely to convert will spend your budget on people who were converting anyway.
Head to decisioning. → -
“Leadership wants one number for marketing effectiveness.”
There is not one. There is a set of estimates with different assumptions and different uncertainty. The useful move is triangulation: take the agreement where methods agree, and be specific about where they do not.
Read the triangulation lesson. →
If the symptom is that every one of these has been answered once and none of the answers survived the quarter, the problem is the machinery rather than the method. The systems section is about that.