From CSV to causal answer
in 90 seconds.

1. 01

   Upload

   Export your last 90 days of Shopify orders and GA4 sessions. 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 90 days of data finishes in about 90 seconds.

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 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.