GRIFFIN / GeoSC 0.3.1
Geo-experiment measurement
Design, assess and interpret geographic marketing experiments in Python.
GeoSC is a Python toolkit and command-line interface (CLI) for designing and analysing geo-level marketing experiments with SparseSC synthetic control. It supports three operational stages: power analysis, donor screening, and inference. The stages share an outcome panel and design decisions, but their artefacts are not automatically passed from one stage to the next.
Use GeoSC only when the treated geography, eligible donor pool, treatment timing, outcome definition, and contamination risks can be defended. A successful command or a small p-value does not establish those conditions.
Choose a route
- New user: install GeoSC, read the concepts and workflow, then complete the first successful run.
- Planning a test: define the estimand and windows, eligible geographies, and run power analysis.
- Running a post-test analysis: prepare the panel, run inference, then apply the design-rejection rules.
- Auditing a run: use the configuration, output schema, and reproducibility references.
- Comparing methods: read method comparisons before comparing GeoSC MDEs or effects with TBR, GeoX, or matched markets.
Repo-local example assets such as data-config/ only work from a source
checkout. Built distributions install the Python package and CLI but do not
ship data-config/, recipes/, or shapemap/.