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Method

From CSV to causal answer
in 5–10 minutes.

Three steps. No pixel. No SDK. The method below is the same one we describe in the methodology document we send any customer who asks.

  1. 01

    Upload

    Export your last 40–90 days of GA4 sessions, and Shopify orders if you have them. Both are point-and-click exports inside the platforms you already use. Drop the files into Causality Engine.

  2. 02

    Analyze

    Our causal-inference model reads the natural variation in your spend and sales (weeks you scaled, weeks you paused, promotions, seasonality) and estimates each channel's incremental contribution. Standard run on 40–90 days of data finishes in 5–10 minutes.

  3. 03

    Decide

    You see incremental ROAS per channel with confidence intervals, a plain-English ranking of what to scale and what to cut, and a downloadable report you can take into a budget meeting.

What the model assumes (and what it does not)

Every causal claim rests on assumptions. Ours are documented, not hidden. The model assumes your spend and sales data are complete and accurate, that your last 40–90 days contain enough natural variation to identify each channel's effect (most do), and that no external factor unobserved in your data is moving sales in lockstep with one channel.

It does not assume your platforms are honest. It does not assume your last-click rule is right. It does not assume causation from correlation. Where an assumption is uncertain, the confidence interval widens.

Why no pixel

Pixels have been steadily degraded by iOS privacy changes, third-party cookie deprecation, and platform-side modeling. Patching them with modeled conversions inside each platform reintroduces the platform's bias. We work from first-party aggregate data instead. It survives every browser change because it never depended on a browser cookie.

Use cases

What you can do with this.

Each line is a workflow a marketing team runs against its own data. No adjectives, no implementation details, no product mechanics. If a line describes what you are trying to do, the rest of this page is the answer.

  • Interpret a saturation curve
  • Interpret a budget-reallocation recommendation
  • Document attribution methodology for review
  • Onboard a new team member to causal measurement
  • Translate platform metrics into incremental terms
  • Translate causal results to ad-platform language
  • Choose a measurement approach for a low-volume brand
  • Choose a measurement approach for a high-volume brand

Find your wasted ad spend in 5–10 minutes.

Watch the model work on a sample store first, no signup. Then upload your last 40–90 days of GA4 sessions and get incremental ROAS with confidence intervals. No pixel, no SDK. €99 per read.

Try the live demoRun it on your data — €99

Prefer to talk it through? Book a 20-min call, or read how it works.