Meta Ads Measurement: Attribution and Incrementality
What is the difference between attribution and incrementality?
Attribution assigns credit under a set of rules; incrementality measures what would not have happened otherwise. Platform attribution answers a bookkeeping question, media mix modelling gives aggregate association, and holdout or geo designs give causal evidence. Each carries different assumptions and different limits.
For the surrounding account decisions, compare Cookieless Attribution: An MER + Incrementality Stack and use Meta Ads Measurement: Attribution Window Comparison as the next diagnostic.
In short
This guide owns one decision artifact: the filled, auditable structure below. Reader-supplied thresholds stay explicit; missing evidence stays missing.
Method matrix
| Method | Question answered | Unit | Assumptions | Data needs | Causal status | Interference / contamination risk | Power limits | When not to use |
|---|---|---|---|---|---|---|---|---|
| Platform attribution | Which outcomes receive credit under this platform rule? | Event, order, or account | Identity and reporting rules are applied as configured | Platform event, spend, and attribution export | Descriptive credit, not causal | Cross-channel credit and unobserved paths can overlap | No experiment power claim | Do not use alone to claim lift |
| MMM | How do aggregate outcome movements associate with channel inputs and controls? | Time period and market aggregate | Model form, controls, and variation are adequate | Longitudinal spend, outcomes, prices, promotions, seasonality | Causal only to the extent identification assumptions hold | Correlated channel changes and omitted variables | Weak variation produces wide uncertainty | Avoid granular campaign verdicts unsupported by the model |
| Randomized user/market holdout | What is the assignment effect for the eligible experiment population? | Randomized user, cluster, or market | Random assignment, stable measurement, declared noncompliance handling | Assignment, exposure, outcomes, exclusions | Causal for the defined estimand when design assumptions hold | Spillover between treatment and control | Rare outcomes or few clusters reduce precision | Do not run where isolation, ethics, or sample support is absent |
| Geo experiment | What incremental outcome difference appears across assigned geographies? | Geography-time cell | Comparable geos, sufficient pre-period fit, limited spillover | Geo spend, outcomes, covariates, assignment | Causal when assignment and analysis support it | Travel, media spill, concurrent local actions | Few independent geos constrain inference | Avoid when markets cannot be isolated or pre-fit is poor |
Treatment outcome rate = (eligible treatment-assigned units with the declared outcome) ÷ (eligible treatment-assigned units). Control outcome rate = (eligible control-assigned units with the declared outcome) ÷ (eligible control-assigned units). Incremental rate = treatment outcome rate − control outcome rate. Source/window: assignment ledger and first-party outcomes through the preregistered maturity cutoff. Limitation: report confidence interval, power, noncompliance, and contamination.
Interpretation boundary
Use the attribution-versus-causal method matrix only for its stated decision. Use platform attribution for credit, MMM for aggregate modeled association, and a suitable randomized or geo design for a defined causal estimand. A causal read is inconclusive when assignment, power, contamination, interference, or outcome denominators are not supportable. Reader-supplied thresholds remain inputs, not universal standards.
Documented thresholds
Meta publishes the figures below; they are reproduced here with the article they come from. Retrieved 2026-09-22 — Meta revises this documentation without notice, so verify anything load-bearing against the current version before you act on it.
- The default window. Meta documents 7-day click or 1-day view as the default for all new ad sets. The selectable alternatives are 1-day click, 7-day click, and 1-day click or 1-day view.
- A wider window reallocates credit, it does not create revenue. Take $5,000 of spend credited with 100 conversions on 1-day click — a reported $50 cost per result. If 7-day click credits the same spend with 140, reported cost falls to $35.71, an apparent 28.6% improvement with no additional sale.
- Why Commerce Manager disagrees. Its tagged-content reporting is fixed at 28-day click plus 1-day view and cannot be changed, and it reports by conversion date while Ads Manager reports by impression date. Both can be correct and still differ on the total and the day.
Source: Meta Business Help Centre — https://www.facebook.com/business/help/854500742637772, https://www.facebook.com/business/help/1997130013754737.
Can software help?
Bach.ai audits your connected Meta account, estimates the revenue impact of what it finds, and proposes specific fixes. It applies a change only after you approve it. Think of it as an automated audit layer that surfaces issues and proposed fixes for your review — not a replacement for your team’s judgment, and creative production is not its core job, though the Pro and Agency plans can generate a limited number of variants.
FAQ
Which measurement method fits attribution, modeling, and causal questions?
Use platform attribution for credit, MMM for aggregate modeled association, and a suitable randomized or geo design for a defined causal estimand.
What makes an incrementality study inconclusive?
A causal read is inconclusive when assignment, power, contamination, interference, or outcome denominators are not supportable.
Can a measurement-method matrix turn attributed revenue into incremental impact?
It supports the bounded operating choice encoded by the attribution-versus-causal method matrix. It cannot replace missing source records or turn platform credit and observed association into incremental impact.
Method and sources
“Attribution assigns credit under a set of rules; incrementality measures what would not have happened otherwise.”
Source: Where this guide describes platform behaviour, it follows Meta’s published advertising and Marketing API documentation, which changes without notice — verify anything load-bearing against the current version before you act on it. Every threshold the guide asks you to supply is first-party, drawn from your own account exports and commerce ledger, because no external benchmark can stand in for your own margin structure.