Which attribution vendors explain their methodology openly instead of a black box?
Openness is checkable. Recast publishes its Bayesian approach in detail. Haus and Measured describe experiment design because the method is the product. Causality Engine names causal inference on a GA4 export and keeps the how its own, but every number in the read checks against your export. Northbeam and Triple Whale describe their models at a level that tells you the family but not the assumptions.
By Joris van Huët, Founder & CEOPublished
The shortlist, compared.
What each tool measures, what it needs installed, and what it costs. Prices are the vendor's own published pricing, read on the date shown.
| Tool | Method | Pixel | Starting price |
|---|---|---|---|
| Causality Engine | Method: Causal inference on a GA4 export | Pixel: No | Starting price: 99 euro per readExcl. VAT |
| Recast | Method: Bayesian marketing mix modeling | Pixel: No | Starting price: Custom (quote) |
| Haus | Method: Geo-lift experiments, with Causal Attribution and Causal MMM calibrated on them | Pixel: No | Starting price: Custom (quote)haus.io/pricing, read |
| Measured | Method: Geo-based incrementality testing | Pixel: No | Starting price: Custom (enterprise) |
| Northbeam | Method: Machine-learning multi-touch attribution | Pixel: Yes | Starting price: $1,500/mo (Starter)northbeam.io/pricing, read |
| Triple Whale | Method: Pixel-based multi-touch attribution | Pixel: Yes | Starting price: Free plan; paid from $219/moapps.shopify.com/triplewhale-1, read |
Competitor pricing is each vendor's publicly listed pricing as read on the date shown, and it changes without notice: verify on the vendor's own site before relying on it. Vendors without a public price are marked as such. Comparisons set Causality Engine's one-time €99 analysis against subscription models.
Why each one is on the list
- Causality Engine. States the method family and the input, keeps the how its own, and lets every number be checked against your own GA4 export.
- Recast. Publishes its Bayesian marketing mix modelling approach in technical detail.
- Haus. Experiment design is the product, so the method is described rather than hidden.
- Measured. Holdout methodology documented, enterprise quoted.
- Northbeam. Machine learning multi touch attribution described by family rather than by assumption.
- Triple Whale. Pixel based multi touch attribution, documentation aimed at operators rather than analysts.
How to choose between them.
- Named family, not a label
- AI and machine learning are not methods. Bayesian marketing mix model, geo lift experiment, rules based multi touch and causal inference on observational data are. A vendor that will not name the family cannot be checked by anyone.
- Stated assumptions
- Every method needs something to be true. Marketing mix models need enough variation in spend. Causal inference on observational data needs the confounders to be measured. A vendor that publishes assumptions is handing you the tools to reject its own output.
- A published failure condition
- Ask what makes the method wrong, and when the vendor will say so. A stated floor, a minimum history, a channel it cannot separate. Openness that only covers the good cases is marketing.
- Reproducible from your own data
- The strongest form is being able to check the result yourself: the same export, the same period, a number you can trace. Methods that require a proprietary pixel history cannot offer this even when they want to.
Questions people ask next.
Which attribution vendors explain their methodology openly instead of a black box?
Recast publishes its Bayesian approach in detail, Haus and Measured describe experiment design because the method is the product, and Causality Engine names causal inference on a GA4 export and keeps the how its own, while every number in the read checks against your export. Most pixel based multi touch vendors describe the family but not the assumptions.Is AI-powered a methodology?
No. It names an implementation, not a method, and it is not testable. The useful question is which family the model belongs to, because each family has a known failure mode you can check your own data against.Why does a published failure condition matter?
Because a method that always returns a confident answer returns one when the data cannot support it. A vendor that publishes where it breaks is the only kind whose successes you can weigh.
Related reading
More questions answered on the answers index, including attribution tools for GA4 and Shopify, measuring incremental revenue by channel, no-code analytics for small teams.
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