choosing-causalpy-methods
Choose the appropriate CausalPy experiment class from a causal question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill choosing-causalpy-methods --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.
# Choosing CausalPy Methods Use this skill to translate a user's causal question into a CausalPy experiment choice. This is the design-intake skill, not the implementation skill. Once the method is chosen, hand off to `running-causalpy-experiments` for constructor details, model configuration, priors, summaries, plots, and interpretation. ## Intake Checklist 1. Restate the estimand: ATE, ATT, loca
What does the choosing-causalpy-methods skill do?
Choose the appropriate CausalPy experiment class from a causal question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill choosing-causalpy-methods --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.