Causal Estimand and Identification

GeoSC reports an unscaled average post-period treated-minus-counterfactual difference. Calling it causal requires more than estimator completion.

For treated set $\mathcal{T}$ and measured post periods $\mathcal{P}$, the target can be written conceptually as

$$ \tau = \frac{1}{|\mathcal{P}|} \sum_{t \in \mathcal{P}} \left(\bar{Y}_{\mathcal{T}t}(1)-\bar{Y}_{\mathcal{T}t}(0)\right). $$

The observed treated outcome supplies $\bar{Y}(1)$. SparseSC estimates the unobserved $\bar{Y}(0)$ from eligible donor outcomes.

A causal interpretation needs a stable outcome definition; treatment timing and assignment measured correctly; donors unaffected by treatment; no uncontrolled geography-specific shock aligned with launch; adequate pre-period support for the counterfactual; and an estimand whose geography and period match the business question. These conditions are design arguments, not outputs of the optimiser.

GeoSC’s parallel-trends diagnostic can be required as an operational gate, but passing it does not prove exchangeability. The interference screen is advisory and has no exposure model. Power addresses detection under a simulated DGP, not identification. State each evidence source separately.