pymc-labs-causalpy

Plugin pymc-labs-causalpy from marketplace cascade-data-analytics in joshuarweaver/cascade-data-analytics. Commands read from the repo's manifest — 1 month ago.

Skills (13)

• 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.

• 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.

• pr-to-green

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

• pr-workflows

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

• python-environment

  Detect, configure, and use a conda-compatible tool. Use before tasks that need the project environment, such as importing project code, running tests, building docs, or invoking repo tooling.

• research-and-planning

  Perform structured research and turn findings into an implementation plan.

• 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, or request changes.

• working-with-marimo

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

• 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?

• 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 about whether a campaign, policy, or interve

• 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.

• 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.

• 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.

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