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.
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Use choosing-causalpy-methods to recommend the best CausalPy experiment for [causal question and estimand] using [data layout and treatment assignment] under [identification assumptions], and state the required columns, risks, and next step.
Required inputs
[causal question and estimand]
[data layout and treatment assignment]
[identification assumptions]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: choosing-causalpy-methodsdescription: 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.
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.