GA4 Attribution Done Right (Causal, Not Last-Click)

Why GA4 attribution rules cannot answer the budget question

• GA4's data-driven model assigns credit using machine learning, but it cannot observe the counterfactual
• Last-click models give 100% credit to the final touchpoint regardless of whether earlier channels created demand
• Position-based, linear, and time-decay models divide credit by rules you did not derive from data
• GA4 reports correlation: which paths preceded conversions. Not causation: which channels caused them
• Lookback windows truncate long consideration cycles common in DTC (14-30 days for beauty, 7-day fashion)

How Causality Engine reads GA4 differently

• Causal inference on the natural variation in your GA4 data (paused channels, spend changes, promotions)
• Incremental ROAS per channel: what would have sold without each channel
• Confidence intervals so you can size decisions correctly
• Works with standard GA4 exports (acquisition by channel, by source/medium)
• Independent of GA4's attribution model selection - you get a causal answer regardless

GA4 CSV upload