Causality Engine vs. Cometly: Attribution Software Compared

Causality Engine vs. Cometly: Attribution Software Compared

Quick Answer: Cometly puts the Comet Pixel on your site, adds server-side tracking and Meta's Conversions API, and splits the credit for each sale across the touchpoints it records, using a model you pick. Causality Engine installs nothing: it reads a GA4 export and estimates what each channel caused, next to what last-click gave it. Cometly is built mainly for B2B SaaS and subscription advertisers who want cleaner conversion signal for Meta and Google; Causality Engine is built for ecommerce budget decisions at the channel level. Pick by the question you have: which touch gets the credit, or which spend caused the sale.

Cometly is a polished, Stripe-native pixel and CAPI tool. It's good at feeding cleaner conversion events back to Meta and Google, with high match quality on Meta's Conversions API, and at attributing MRR and LTV for subscription and SaaS businesses through Stripe. Credit is assigned with touch-crediting models: first click, last click, linear, U-shaped and data-driven. Setup is guided rather than self-serve: the Comet Pixel, server-side tracking, and webhook and click-ID setup per store, and the vendor cites the CRM and ad-platform setup as the reason there is no free trial. Pricing is usage-based on monthly pageviews and quoted on a call (cometly.com/pricing, as read on 2026-09-08); Cometly's affiliate page puts a typical Core signup at $750/month (cometly.com/affiliates, as read on 2026-09-26).

What a touch-crediting model can't tell you is which sales would have happened anyway. It decides how to split credit between the touchpoints it saw; it doesn't estimate what a channel added. That's the line between marketing attribution by correlation and causal attribution.

A correlation-based system can show that Instagram ads come before a lot of purchases. It can't tell whether the ads caused them or coincided with something else: a seasonal sale, an influencer mention, a competitor running out of stock. A confounding variable moves both the supposed cause and the effect. Raise Facebook spend in the same weeks a PR push lands, and the ads look better than they are. The textbook case is ice cream sales and shark attacks, which rise together because both follow the summer. However detailed a multi-touch model gets, it splits credit; it doesn't show whether a channel brought in new customer acquisition or caught buyers who'd have come anyway.

Privacy rules made the tracking side harder. Since Apple's App Tracking Transparency, iOS apps need permission to track people across other companies' apps and sites, so ad platforms see fewer of the journeys they used to connect, and a sale claimed by two platforms can have no shared identifier to de-duplicate it. That makes campaign effectiveness harder to read from clicks alone. Tracking more precisely helps the platforms optimize; it doesn't tell you what a channel added. For that, the attribution has to estimate what would have happened without the channel.

Causality Engine does that from data you already have. It uses proprietary causal inference on a GA4 export: no pixel, no SDK, and the read arrives 1–2 minutes after upload. For each channel it shows what that channel caused, as its share of the credit once Direct is split back to the channels that sent those buyers, next to last-click rebuilt from the same export: within 25% the two agree, and under 1% on both is too small to act on. Every channel gets a data-health score from 0 to 100, which grades the read rather than the channel, and a next step with how to test it: raise, cut or hold, or fix tagging first. The read adds the common paths to purchase from GA4, and receipts that check every number against your export.

What it doesn't do: it works on GA4's channel groups (Paid Search, Paid Social, Email and the rest), not on campaigns, keywords, creatives or audiences. It takes no spend, so it reports no ROAS. It has no direct connections to Shopify or the ad platforms today; those are on the roadmap. And it doesn't do Cometly's jobs of sending conversion events to Meta and Google or attributing Stripe MRR. A read on a GA4 export costs €99 once; Pro is €299/month, billed monthly. Prices exclude VAT.

Here is the comparison side by side:

Choose Cometly if you're a SaaS or subscription advertiser whose main problem is feeding cleaner conversion events back to Meta and Google, and you want Stripe-tied MRR and LTV attribution on top of pixel-grade tracking. Choose Causality Engine if the question is what each channel caused, set next to what last-click gave it, on the GA4 history you already have. The two can run side by side: one tool for in-platform optimization, the other as an independent check before you move budget.

Keep in mind that every platform grades its own homework. Cleaner conversion events make Meta's and Google's reports more complete, but they're still Meta's and Google's reports, and each counts the purchases its own ads touched.

Rule of thumb: before you buy either tool, put last week's Meta- and Google-reported purchases next to your Shopify orders for the same week, then cut one channel's spend in one region for two weeks and compare revenue with the regions you left alone. The first shows how far the platform numbers run ahead of your orders; the second shows what that channel caused, for free.

Frequently Asked Questions

What is the core difference between correlation-based and causal inference attribution? Correlation-based attribution records which touchpoints came before a sale and splits the credit between them; causal inference estimates whether the sale would have happened without them. A correlation model can show that customers who saw a Facebook ad later converted. A causal estimate asks whether the ad caused the conversion or whether it would have happened anyway, which is the part a budget decision needs.

How does Causality Engine handle data privacy changes like iOS 14+? It sidesteps the pixel but not GA4. The read works on an aggregated GA4 export, with no pixel and no user-level tracking of its own, so it doesn't depend on following individual people across apps and sites. It still sees only what GA4 recorded, so consent choices and tagging gaps carry into the read, and each channel's data-health score shows how far to trust it. For background, see the glossary entry on the third party cookie.

Is Causality Engine suitable for smaller DTC brands or only large enterprises? It suits both: there is no minimum spend, revenue or team size. A read costs €99 once, refundable within 30 days, no questions asked, and Pro is €299/month, billed monthly, when you want the read updating continuously. Enterprise is a 90-day pilot on a custom quote.

Can Causality Engine integrate with my existing marketing platforms like Shopify, Facebook Ads, and Google Ads? Not directly today. It reads a GA4 export, which already holds the sessions and purchases those platforms send to your store. Direct connections to Shopify, Meta, Google and TikTok are on the roadmap, not live.

How long does it take to see results with Causality Engine? The read arrives 1–2 minutes after you upload the GA4 export, with no setup or calibration period and nothing to install. What it changes in your results depends on the budget moves you test from it, so there is no promised timeline for ROAS or customer acquisition cost.

What level of technical expertise is required to use Causality Engine? None beyond exporting a report from GA4. The read explains itself in plain terms: what each channel caused, last-click beside it, a data-health score and a next step per channel, and receipts that check every number against your export. Pro adds priority email support.