Causal Identification
An Abacus fit estimates quantities conditional on the specified likelihood, priors and data. Interpreting a media contrast as the effect of an intervention requires a separate identification argument. Good predictive fit, stable sampling and narrow posterior intervals do not supply that argument.
Define the intervention and identifying variation
State the channel, spending change, dates, population and outcome for the causal question. Specify how carryover and changes in other channels enter the contrast. Then explain why the observed variation identifies that effect.
For observational MMM, consequential assumptions include:
- an adequate control set for common causes of media and outcomes, without conditioning on mediators or colliders that invalidate the intended effect;
- enough relevant variation to learn the response in the spending range of interest, rather than relying entirely on extrapolation;
- consistent treatment/outcome definitions, and appropriate treatment of interference, carryover and anticipation;
- an adequate response, baseline and error specification for the estimand.
For example, spending that responds to expected demand can be associated with higher sales even without a media effect. Adding a smooth trend does not necessarily account for that demand information. A control associated with both variables is useful only if its causal role and measurement justify conditioning on it. These assumptions require subject-matter evidence; residual checks alone cannot establish them. See the primary discussion of MMM causal assumptions.
The named FE estimator removes time-invariant unit effects from its slope likelihood. CRE adjusts for its declared transformed between-unit summaries. Neither removes arbitrary time-varying confounding. See Choose an Estimator and Baseline vs Media Trade-Offs.
Assess designs by their assumptions
There is no universal ranking of methods that substitutes for assessing the design and estimand.
| Design | Key distinction |
|---|---|
| Randomised experiment | Assignment supports an identification argument for the specified treatment contrast; adherence, missing outcomes, interference and the analysis population still matter |
| Matched-market study without random assignment | Matching does not create randomisation; identification depends on the design’s comparability and counterfactual assumptions |
| Instrumental variables | Requires relevance, independence and exclusion; some estimands also require monotonicity or further structural assumptions |
| Difference-in-differences | Requires an appropriate untreated-trend assumption and treatment-timing conditions; compatible pre-trends do not prove parallel counterfactual trends |
| Regression discontinuity | Identification near a cutoff relies on the assignment mechanism and continuity or local-randomisation assumptions; extrapolation requires more |
Diagnostics can challenge aspects of a design. They do not generally prove exclusion, absence of confounding or unobserved counterfactual behaviour. Effects identified for different populations or interventions are not interchangeable merely because each estimate is credible for its own task.
Use calibration for the evidence it supplies
Abacus exposes mmm.add_lift_test_measurements(...) and
mmm.add_cost_per_target_calibration(...) on a built model. Both are unavailable
for the named FE, CRE and release-gated RE presets. Follow
Calibration for supported inputs and
workflow. EventAdditiveEffect adds an event component; it does not attach
experimental lift evidence.
Before calibration, align the external estimate with the model’s channel, units, spending contrast, time window and outcome. Assess the design’s identification, uncertainty and relevance to the modelling population. Avoid counting the same evidence twice without an appropriate dependence model.
Calibration conditions the fitted response on the supplied evidence under the calibration model. Conflicting observations and calibration data need investigation; their combination is not guaranteed to remove bias. A test for one channel and window does not identify every other channel or establish transportability to a different spending range. Record these limits alongside the calibrated result.
Interpret scenarios and optimisation conditionally
A scenario evaluates a specified spending plan under the fitted response functions and planning assumptions. Optimisation selects a plan for a chosen objective and constraints. Neither operation independently verifies that the intervention will produce the predicted change.
A channel ranking at historical spend is insufficient for allocation. The optimum depends on marginal responses across feasible spending levels, saturation, carryover and constraints. Two models can rank historical returns in the same order yet recommend different allocations. Proportional bias in some reported channel estimates does not establish that their full marginal response functions are correct.
Report the estimand, units, assumptions, supported spending range and uncertainty. Posterior intervals condition on the fitted model; they do not automatically include uncertainty about omitted confounding, model choice or future structural changes. Use sensitivity analyses and relevant experimental evidence to assess those risks. See Interpreting Optimisation.