[{"data":1,"prerenderedAt":44},["ShallowReactive",2],{"article-en-attribution-vs-causality":3},{"slug":4,"locale":5,"title":6,"description":7,"category":8,"categoryKey":8,"readMinutes":9,"publishedAt":10,"body":11,"sources":37,"seoTitle":6,"seoDescription":7},"attribution-vs-causality","en","Attribution vs Causality: The Report Says Who Got Credit. The Experiment Asks Who Caused Growth.","Why attribution models and causal measurement answer different questions—and why serious marketing teams need both.","Measurement",10,"2026-08-12",[12,17,22,27,32],{"h":13,"p":14,"bullets":16},"Attribution is accounting for journeys",[15],"Attribution asks how observed conversions should be assigned across observed touchpoints. That is useful for reporting and operational visibility. But the presence of a touchpoint before a sale does not prove the touchpoint caused the sale. High-intent customers naturally interact with more brand and search surfaces, which can make channels look more powerful than they are.",[],{"h":18,"p":19,"bullets":21},"Causality asks for the missing world",[20],"The causal question is counterfactual: what would have happened to the same market, customers or geographies if the marketing treatment had not happened? We cannot observe both worlds for the same unit at the same time. Experiments create a defensible comparison by withholding or changing treatment for a control group.",[],{"h":23,"p":24,"bullets":26},"Why platform return can be higher than incremental return",[25],"A platform can correctly record a purchase after an ad click and still overstate the sale caused by advertising. The customer might have bought anyway. Attribution describes the path we observed; incrementality estimates the change created by the marketing intervention.",[],{"h":28,"p":29,"bullets":31},"Use both, but assign them different jobs",[30],"Use attribution to monitor flows, diagnose delivery and operate campaigns. Use experiments, lift studies, geo tests or calibrated models to make budget claims about causal impact. When the two disagree, do not ask which dashboard is “wrong.” Ask which question each method was built to answer.",[],{"h":33,"p":34,"bullets":36},"The leadership question",[35],"Instead of “which channel has the best reported return?” ask “how much business outcome disappears if we remove or reduce this activity?” That change in language moves the team from credit allocation toward decision science.",[],[38,41],{"title":39,"url":40},"Google Meridian — causal inference","https:\u002F\u002Fdevelopers.google.com\u002Fmeridian\u002Fdocs\u002Fcausal-inference\u002Frationale-for-causal-inference-and-bayesian-modeling",{"title":42,"url":43},"Google Ads Experiment Center","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F16856494?hl=en",1786583029881]