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Generates causal inference practice exercises with simulated data and known ground truth. Supports experiments, DiD, IV, RDD, synthetic control, matching, time series, and DAG reasoning across Basic/Intermediate/Advanced difficulty.
npx claudepluginhub robsontigre/everyday-causal-skills --plugin everyday-causal-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/everyday-causal-skills:causal-exercisesThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate realistic causal inference exercises with simulated data. The true effect is known, so practitioners can verify their work.
Guides users through a structured interview to identify causal problems and recommend inference methods with step-by-step analysis plans.
Designs, runs, and critiques causal inference workflows in Stata for identification strategies, treatment effects, DiD, IV, event studies, RD, and assumption-sensitive empirical claims.
Guides advanced data science workflows including EDA, statistical analysis, ML modeling (supervised/unsupervised/deep learning), time series, causal inference, and deployment.
Share bugs, ideas, or general feedback.
Generate realistic causal inference exercises with simulated data. The true effect is known, so practitioners can verify their work.
references/lessons.md — known mistakes. Do not repeat them.references/dgp-library.md — available data-generating processes.references/method-registry.md — method details.Ask: "What difficulty level? (Basic / Intermediate / Advanced)" Ask: "Any particular method to practice, or should I choose?"
Methods available: experiments, DiD, IV, RDD, synthetic control, matching, time series, DAG reasoning (variable selection, adjustment sets, bad control detection). Ask: "R or Python?"
Select a DGP from references/dgp-library.md matching the difficulty and method. Present a realistic business narrative. Do NOT reveal the method, the DGP, or the true effect.
"Scenario: You work as a data analyst at [company]. [Business context narrative]. Your manager wants to know: [causal question]. You have access to the attached dataset."
Run the DGP code (using Bash tool) to generate the dataset. Save files:
docs/causal-exercises/YYYY-MM-DD-<exercise>/data.csv — the datasetdocs/causal-exercises/YYYY-MM-DD-<exercise>/dgp.[R|py] — the DGP code (DO NOT show to user yet)docs/causal-exercises/YYYY-MM-DD-<exercise>/solution.md — true effect and method (DO NOT show yet)Tell the user: "I've generated the dataset at [path]. Take a look and tell me: What causal method would you use and why?"
If the user asks for help, provide hints in order:
After the user presents their analysis (or asks for the answer):
Save debrief to docs/causal-exercises/YYYY-MM-DD-<exercise>/debrief.md.
Before this skill:
/causal-planner -- Optional; user may come directly to practiceAfter this skill:
/causal-[method] -- Apply the practiced method to real data/causal-dag -- Practice drawing DAGs, identifying adjustment sets, and detecting bad controlsIf the user identifies a problem with the exercise (e.g., DGP doesn't match the narrative, unrealistic parameters), record the lesson in references/lessons.md.