Run Power Analysis

Define a plausible effect grid and duration grid, then run:

geosc power --config data-config/power_analysis_config.yaml --jobs -1

Use a fixed random_seed for an audited run. Seeded runs use CPU simulation paths. Start with a small n_simulations only to debug the configuration, then increase it until Monte Carlo intervals are adequate for the decision.

For each row:

1. require valid: true;
2. inspect failure_rate and warnings;
3. compare power with its Wilson interval;
4. verify the DGP rank, explained variance, backends, placebo support, and injection metadata;
5. interpret MDE as the smallest tested effect reaching target_power for a duration.

GeoSC does not interpolate between effect sizes. If no valid grid value reaches the target, report that the target was not reached on the tested grid. If all rows are invalid, report MDE as unavailable. The CLI’s default planning threshold is 80%; it is configurable through target_power and is not a statistical property of SparseSC.

Power uses the SparseSC estimator and p-value detection rule inside each simulation, but it does not execute the full inference workflow. It does not run inference data policies, cooldown resolution, assumption checks, result processing, or plotting. Align the power input panel and intervention split with the intended inference design yourself.