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.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill running-causalpy-experiments --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.
# Running CausalPy Experiments Use this skill when the CausalPy experiment class is already known or has just been selected by `choosing-causalpy-methods`. This skill is for execution: preparing data, instantiating the experiment, choosing a model backend, setting sane priors, inspecting outputs, plotting, and communicating results. ## Workflow 1. Load and validate a pandas `DataFrame` with the da
What does the running-causalpy-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill running-causalpy-experiments --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.