Meta Ads Measurement: Attribution and Incrementality

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.

Can software help?

Bach.ai audits your connected Meta account against 100+ checks, ranks what it finds by estimated impact, and proposes specific fixes. It stays read-only until you approve a change, then executes the approved change on Meta; connected Google Ads data is used for intelligence only. 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.