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.
Data & Analytics
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Updated Jan 11, 2026, 09:59 PM
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---
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](reference/placebo_in_time.md): Full code for the `PlaceboAnalysis` class, usage examples, and hierarchical status-quo modeling.
