pymc-labs/CausalPy

pr-to-green

Bring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.

feature-exploration

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Use when implementation details are unclear and can be resolved by reading docs, inspecting code, and running focused experiments.

review-pr

Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns. Use when asked to review a PR, assess a branch before merge, summarize PR risks,…

causal-detective

Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks. Use when validating whether a causal effect is real or when the user asks "is this effect real?" or "can I trust this result?"

running-placebo-analysis

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.

choosing-causalpy-methods

Choose the appropriate CausalPy experiment class from a causal or impact question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled, including plain-English questions…

example-datasets

Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes. Use when the user needs sample data or asks which demo datasets are available.

python-environment

Detect, configure, and use the project's Python environment (uv by default, conda-compatible tool as a fallback). Use before tasks that need the project environment, such as importing project code, running tests, building docs, or invoking repo tooling.

running-causalpy-experiments

Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.

pr-workflows

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

github-issues

Create, evaluate, and triage GitHub issues for CausalPy. Use when filing a bug, proposing an enhancement, analyzing existing issues, or splitting large work into parent-child sub-issues.

research-and-planning

Perform structured research and turn findings into an implementation plan.

working-with-marimo

Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.