End-to-End Synthetic Study

This tutorial uses the repository’s synthetic panel to exercise all three stages and their explicit hand-offs.

1. Inspect the stage configs

The pipeline requires three canonical filenames in one directory:

• power_analysis_config.yaml;
• donor_eval_config.yaml;
• geolift_analysis_config.yaml.

Confirm that all three identify the same outcome, geography universe, treatment units, and launch date. The key names differ by stage; use the stage-specific configuration reference.

2. Run the pipeline

geosc pipeline \
  --config data-config/geolift_analysis_config.yaml \
  --output-dir outputs/tutorial_run \
  --no-progress

The command runs power, donor evaluation, and inference independently. It then summarises their artefacts. It does not feed recommended donors into inference.

3. Review the design outputs

Open multicell_power_analysis/power_analysis_results.csv. Retain only rows where valid is true. MDE is the smallest tested effect_size that reaches target_power for a duration; it is grid-based and conditional on the simulated DGP.

Open multicell_donor_eval/donor_pool_quality.json. Treat quality_assessment: "INSUFFICIENT", incomplete metrics, dominant recommendation weights, or poor overlap as redesign signals.

4. Review inference

Open multicell_geolift_analysis/geolift_results.json. The primary estimate is the unscaled post-period average effect (att). The top-level p_value is the two-sided in-space placebo p-value for that average effect. Scaled quantities remain diagnostics and must not be combined with the unscaled interval.

5. Record the hand-off

If the donor screen changes eligibility, materialise a new panel containing the treated units and approved donors. Rerun power and inference on that same panel. Record the input hash, configs, exclusions, software version, and random seeds.