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.
npx skills add majiayu000/claude-skill-registry --skill running-placebo-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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.
- Workflow
- Key Concepts
- References
What does the running-placebo-analysis skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill running-placebo-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.