Is Haus Causal Attribution the same as Causality Engine?

No. Both aim at what a channel caused rather than what a platform credits it with, and they get there differently. Haus runs geo experiments and uses the results to adjust your platform reporting, refreshed daily. Causality Engine applies causal inference to the GA4 history you already have, with no test to run, and sets what each channel caused next to what last-click gave it.

Is a Haus geo test more accurate than a causal read of GA4 data?

A well-designed, well-powered geo test is the stronger evidence, because randomising regions removes confounders instead of adjusting for them. A read of observational history is bounded by the assumptions in the model, which is why ours grades itself with a data-health score on every channel. The practical question is which one you can run now, and on what budget.

Can I use Causality Engine and Haus together?

Yes, and the order matters. Run a €99 read across every channel first to see which ones look like they caused little, then spend the experiment budget testing those. Testing everything is slow and costly; testing the suspects first is not.

How much does Haus cost compared to Causality Engine?

Haus publishes no price. Its pricing page lists four plans, Basics for Shopify, Core, Plus and Enterprise, and each starts with a demo. Causality Engine is €99 per read, excluding VAT, with a full refund within 30 days, no questions asked, or €299 a month for Pro.

Does Haus work for brands outside the US?

Its pricing page lists Basics, the plan for self-starter DTC brands on Shopify and Amazon, as US only, and international testing on Core, Plus and Enterprise. Causality Engine needs no regions at all: it reads one GA4 export.

Do I have to hold back ad spend to use Causality Engine?

No. A geo holdout keeps the tested ads out of control regions so there is something to compare against. We read the history you already have, so nothing is switched off and no region goes without ads to produce the answer.

Both say causal. One runs tests, one reads your history.

Choose Haus when you can hold ads back in some regions for weeks at a time, the budget decision is big enough to justify a measurement partner, and you want daily numbers calibrated by real experiments. Choose Causality Engine when you want an answer today from the spend you have already made, without a demo or a test to design.

Get your first finding free Or book a 30-min call.

€99 once, excl. VAT. Keep Haus; use us as the causal second opinion.

Same question, different answer. Often the opposite answer.

Causality Engine compared with Haus
Compared on	Causality Engine	Haus
Question answered	What did each channel cause, across your history?	What did this channel cause in the test, carried into daily reporting?
Method	Causal inference on existing GA4 history	Geo experiments, with Causal Attribution and Causal MMM calibrated on them
Evidence strength	Observational, graded by a data-health score	Experimental: GeoLift randomises test and control regions
Held back to learn	Nothing	In a holdout test, the tested ads in the control regions
Time to first number	1 to 2 minutes	Weeks per test: about 2 to 3 for search, 6 to 8 for Meta reach
Price	€99 per read · €299/mo ProExcl. VAT	No public price; four plans, each through a demohaus.io/pricing, read 2026-09-26
Where it runs	Ecommerce brands on GA4, on any store platform	Basics: geo tests in the US only; international from Core

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.

The short version. What each one is for.

Haus is an incrementality platform built on experiments. Its GeoLift tests assign test and control regions by random stratified sampling, then compare the regions that saw your ads with the regions that did not. It carries those results into daily reporting it calls Causal Attribution, which adjusts your platform numbers for incrementality, and into Causal MMM on its Plus and Enterprise plans. A well-run geo experiment is stronger evidence of what a channel caused than a read of past data, ours included. We are not going to pretend otherwise.

The trade is time, access and what the test holds back. Haus publishes no price: each of its four plans, from Basics for Shopify to Enterprise, starts with a demo, and Basics runs geo tests in the US only. By Haus's own guidance a test runs about 2 to 3 weeks for search and 6 to 8 weeks for Meta reach campaigns, and in a holdout test the control regions go without the tested ads while it runs.

Causality Engine answers from the other end: causal inference on the GA4 history you already have. No test to design, no region held back, nothing to install. What each channel caused, next to what last-click gave it, with a data-health score and a next step for every channel, in 1 to 2 minutes, for €99 once.

Which one is the right call for your situation.

Choose Haus when

you can hold ads back in some regions for weeks at a time, the budget decision is big enough to justify a measurement partner, and you want daily numbers calibrated by real experiments. If a major budget line turns on the answer, run the experiment. Inference on observational data is not a substitute for a well-powered randomised test.

Choose Causality Engine when

you want an answer today from the spend you have already made, without a demo or a test to design. It also works as the cheap first pass: a €99 read shows which channels look like they caused little, and the experiment budget then goes on confirming those. That order costs less and tests the right things.

Take it to your CFO.

A measurement partner priced through a demo, with weeks of holdout behind each answer, against €99 on a card and an answer in 1 to 2 minutes, refundable within 30 days. If the read and a later geo test disagree, you learned that for €99.

The questions buyers ask about us vs Haus.

Is Haus Causal Attribution the same as Causality Engine?
No. Both aim at what a channel caused rather than what a platform credits it with, and they get there differently. Haus runs geo experiments and uses the results to adjust your platform reporting, refreshed daily. Causality Engine applies causal inference to the GA4 history you already have, with no test to run, and sets what each channel caused next to what last-click gave it.
Is a Haus geo test more accurate than a causal read of GA4 data?
A well-designed, well-powered geo test is the stronger evidence, because randomising regions removes confounders instead of adjusting for them. A read of observational history is bounded by the assumptions in the model, which is why ours grades itself with a data-health score on every channel. The practical question is which one you can run now, and on what budget.
Can I use Causality Engine and Haus together?
Yes, and the order matters. Run a €99 read across every channel first to see which ones look like they caused little, then spend the experiment budget testing those. Testing everything is slow and costly; testing the suspects first is not.
How much does Haus cost compared to Causality Engine?
Haus publishes no price. Its pricing page lists four plans, Basics for Shopify, Core, Plus and Enterprise, and each starts with a demo. Causality Engine is €99 per read, excluding VAT, with a full refund within 30 days, no questions asked, or €299 a month for Pro.
Does Haus work for brands outside the US?
Its pricing page lists Basics, the plan for self-starter DTC brands on Shopify and Amazon, as US only, and international testing on Core, Plus and Enterprise. Causality Engine needs no regions at all: it reads one GA4 export.
Do I have to hold back ad spend to use Causality Engine?
No. A geo holdout keeps the tested ads out of control regions so there is something to compare against. We read the history you already have, so nothing is switched off and no region goes without ads to produce the answer.