Attribution without a tracking code: what change history tells you

Every ad platform keeps a change history: what was changed, when, and by whom. That history is rarely used for anything besides an after-the-fact audit, even though it is also the missing link between a hypothesis and the result it predicted.

The problem this solves: a metric can improve without the proposed change ever having been made, and a metric can stay flat while the change was made but gets overshadowed by something else. Without pulling in the change history, an improved metric after accepting a hypothesis is not confirmation, at best a coincidence that looks suspiciously like one.

The approach is not complicated, but it does require discipline: classify every change by the type the hypothesis predicted (budget, bid, status, keyword), limit that to the window between accepting the hypothesis and the measurement moment, and treat "no matching change found" as its own outcome, not a hidden "no". That last step is where most attribution attempts run aground: an unexecuted hypothesis still gets judged on numbers that had nothing to do with it.