name: running-placebo-analysis description: Performs placebo-in-time sensitivity analysis to validate causal claims. Use when checking model robustness, verifying lack of pre-intervention effects, or ensuring observed effects are not spurious.

Running Placebo Analysis

Executes placebo-in-time sensitivity analysis to validate causal experiments.

Workflow

1. Define Experiment Factory: Create a function that returns a fitted CausalPy experiment (e.g., ITS, DiD, SC) given a dataset and time boundaries.
2. Configure Analysis: Initialize PlaceboAnalysis with the factory, dataset, intervention dates, and number of folds (cuts).
3. Run Analysis: Execute .run() to fit models on pre-intervention data folds.
4. Evaluate Results: Compare placebo effects (which should be null) to the actual intervention effect. Use histograms and hierarchical models to quantify the "status quo" distribution.

Key Concepts

• Placebo-in-time: Simulating an intervention at a time when none occurred to check if the model falsely detects an effect.
• Fold: A slice of pre-intervention data used to test a placebo period.
• Factory Pattern: Decouples the placebo logic from the specific CausalPy experiment type.

References

• Placebo-in-time Implementation: Full code for the PlaceboAnalysis class, usage examples, and hierarchical status-quo modeling.