Input Schema
Inference and power
Both stages consume a long CSV and pivot it to a unit-by-period outcome matrix.
| Field role | Required property |
|---|---|
| Geography | Stable scalar identifier; treatment IDs must match after the stage’s documented coercion. |
| Period | Parseable date or time label; inference canonicalises to a sorted, timezone-naive DatetimeIndex. |
| Outcome | Numeric and finite under the configured data policy. |
| Covariates | Optional inference columns named by covariate_col_names. |
Power currently uses DataFrame.pivot, so duplicate unit-period rows fail and
missing cells remain in the wide matrix. It does not apply inference’s explicit
duplicate or missing-outcome policies. Validate and canonicalise the panel
before power analysis.
Donor evaluation
The donor stage expects literal source columns date, location, and either
Y or metric. It uses rows before treatment_date. Each treatment-donor pair
is aligned on unique parsed dates; duplicates or insufficient finite overlap
produce incomplete metrics.
Recommended external contract
Store the panel frequency, timezone policy, outcome unit, currency, attribution logic, geography version, extract timestamp, source owner, and row-count checks alongside the CSV. These are project data-governance requirements, not fields GeoSC can infer.