choosing-causalpy-methods
choosing-causalpy-methods is an Experimentation & statistics skill for AI agents, published by pymc-labs in the CausalPy repository on GitHub. Skillspace rates choosing-causalpy-methods D (68/100) and it passed the safety scan; the rating is based on static analysis only.
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 intervention worked.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 1,162
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 88 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Choosing experiment classes in the public CausalPy library
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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