Selects the appropriate quasi-experimental method (DiD, ITS, SC) based on data structure and research questions. Use when the user is unsure which method to apply.

Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos.

Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.

Turn issues into PRs, handle commits, and run prek checks consistently.

Detect and configure a conda-compatible tool, create the CausalPy environment, and run commands inside it. Use before any task that requires Python execution.

Perform structured research and turn findings into an implementation plan.

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.