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.
SkillScore82Maintained
Automatic
◆Data Analyst pack
Last verified 8/11/2026
What it changes
Passed 18 of 22 test cases with the skill, and 7 of 22 without it.
Without skill32%
With skill82%
Checks it passed that the model alone missed
Refrains from generating Python code.
Routes implementation to running-causalpy-experiments.
Directs implementation to running-causalpy-experiments.
and 11 more in the full report
11 fixed7 passed either way4 still failing0 broke
Pass-rate lift
+50 pp
Tokens
-7%
Agent turns
0%
Response time
-33%
Model gemini-3.6-flashMeasured 8/11/2026v118 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 11
Prompt
I want to estimate the causal effect of a scholarship program where students automatically qualify if their standardized test score reaches 1200 or higher. I have a dataset of student test scores, scholarship receipt status, and graduation rates. Which CausalPy method should I use? Please break down your suggestion by specifying the best fit method, the rationale behind it, required column structure, core assumptions required for validity, potential risks or failure modes, and guidance on next steps.
What was checked, without and with the skill
Checked
Without skill
With skill
Recommends RegressionDiscontinuity for the standardized test score threshold.
Passed
Passed
Routes implementation to running-causalpy-experiments.
Missed
Passed
Refrains from generating Python code.
Missed
Passed
case-02 · gemini-3.6-flash · 8/11/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md41 linessingle file
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
Restate the estimand: ATE, ATT, local threshold effect, treatment-on-treated over time, cumulative impact, or a policy/campaign lift.
Identify the data shape: single time series, wide panel of units, long panel of unit-time rows, cross-section, or pre/post group data.
Identify treatment assignment: known intervention time, staggered adoption, threshold/cutoff, kink, instrument, observed treatment with confounders, or treated unit plus donor pool.
Check the identifying story: parallel trends, no anticipation, no manipulation at cutoff, valid instrument, overlap/positivity, convex hull/donor support, or trend continuity.
Recommend one primary CausalPy experiment and any plausible alternatives, then explain the extra data or assumptions needed to choose among them.
Fast Routing
One treated time series, known intervention time, no donor pool: InterruptedTimeSeries.
Known level/slope changes in one time series, especially multiple interruptions: PiecewiseITS.
Treated and control groups observed before and after one intervention: DifferenceInDifferences.
Units adopt treatment at different times: StaggeredDifferenceInDifferences.
One or more treated units with multiple untreated donor units in wide panel format: SyntheticControl.
Synthetic-control setting where both unit weights and pre-period time weights are part of the design: SyntheticDifferenceInDifferences.
Panel regression or fixed-effects adjustment is the target rather than a named quasi-experimental design: PanelRegression.
Pretest/posttest nonequivalent groups with a baseline outcome: PrePostNEGD.
Treatment assigned by crossing a cutoff in a running variable: RegressionDiscontinuity.
Treatment intensity changes slope at a threshold rather than jumping in level: RegressionKink.
Treatment is endogenous but there is a credible instrument: InstrumentalVariable.
Observational binary treatment with measured confounders and overlap: InversePropensityWeighting.
Output Pattern
When you use this skill, return:
Recommended method: name the CausalPy experiment class.
Why it fits: tie the recommendation to data shape, assignment mechanism, and estimand.
Required columns/data layout: list the minimal data structure needed.
Key assumptions: state what must be credible for a causal interpretation.
Main risks: name obvious failure modes or sensitivity checks.
Next step: route to running-causalpy-experiments and the relevant method reference.