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name: running-placebo-analysis description: Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.

Running Placebo Analysis

Executes placebo-in-time sensitivity analysis using the core PlaceboInTime check. Builds a hierarchical Bayesian model of the "status quo" (no-effect) distribution, then compares the actual intervention effect against that learned null. Optionally computes Bayesian assurance (operating characteristics).

Workflow

1. Fit your experiment: Run a CausalPy experiment (ITS, SC) with a PyMC model.
2. Configure the check: Create a PlaceboInTime with n_folds, optional experiment_factory, and optional assurance parameters.
3. Run: Call .run(experiment) (standalone) or use within a Pipeline + SensitivityAnalysis.
4. Evaluate: Inspect the null distribution (theta_new), p_effect_outside_null, and optional assurance results.

Key Concepts

• Placebo-in-time: Simulating an intervention at a time when none occurred to check if the model falsely detects an effect.
• Hierarchical null model: A Bayesian model fitted on fold-level summaries that characterises the distribution of effects under no intervention.
• Assurance: Bayesian operating characteristics — the probability of correctly detecting a real effect given your expected-effect prior and ROPE.
• Factory Pattern: Decouples the placebo logic from the specific CausalPy experiment type.

References

• Placebo-in-time Implementation: Core API reference, usage examples, and hierarchical status-quo modeling.