Best Linear Attribution Alternative for Shopify eCommerce in 2026
Linear attribution is one of the rules-based models for deciding where credit is due. This post sets it beside a causal approach for Shopify stores and compares what each one measures.
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By Joris van Huët, Founder & CEOUpdated 2 min read
Why Linear Attribution Falls Short for Modern eCommerce
While Linear Attribution has been a popular choice, it relies on correlational data, which is increasingly unreliable in a privacy-centric world.
The Problem with Correlation-Based Attribution
Correlation does not equal causation. Linear Attribution and similar platforms use models like last-click or multi-touch attribution, which are essentially rules-based systems that guess where credit is due. They fail to measure the incremental lift from your marketing efforts.
Causality Engine: Causal Inference
Causality Engine, based in the Netherlands, is built on causal inference.
How It Works: A Technical Overview
Causality Engine reads one file, the Attribution paths export from GA4, and shows what each channel caused next to what last-click gave it.
| Feature | Linear Attribution | Causality Engine |
|---|---|---|
| Methodology | Correlation (Rule-based) | Causal Inference |
| Pricing Model | Often complex/seat-based | Transparent: €99 or €299/mo |
Making the Switch
You can start with a one-time analysis for just €99 to see the power of causal inference on your own data.
Related Resources
Best First Click Attribution Alternative for Shopify ecommerce in 2026
Best Last Click Attribution Alternative for Shopify ecommerce in 2026
Best Rockerbox Alternative for Shopify ecommerce in 2026
Best Wicked Reports Alternative for Shopify ecommerce in 2026
Frequently asked questions
How long does it take to see results?
Results arrive 1 to 2 minutes after the upload, and there is no pixel and no SDK to install.What is the main difference between causal inference and traditional attribution?
Traditional attribution, like that used by Linear Attribution, assigns credit based on rules (e.g., last click). Causality Engine uses causal inference on the GA4 history you already have to estimate what each channel caused. Learn more on our /resources/causal-inference-101.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal AnalysisCausal Analysis identifies true cause-and-effect relationships in data, moving beyond correlation to show how marketing actions directly impact outcomes.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- First Click AttributionFirst Click Attribution assigns all conversion credit to the first marketing touchpoint. Causal inference evaluates if first touchpoints truly drive conversions or if other interactions have greater causal impact.
- Last Click AttributionLast Click Attribution: Assigns all credit for a conversion to the final marketing touchpoint before that conversion.
- Linear AttributionLinear Attribution assigns equal credit to every marketing touchpoint in a customer's conversion path. This model distributes value uniformly across all interactions.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a complete view of channel impact on conversions.