# Running Placebo Analysis

> 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.

- Skill: `majiayu000/running-placebo-analysis` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/running-placebo-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/running-placebo-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/running-placebo-analysis

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# 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.