Choosing Causalpy Methods

Choosing CausalPy Methods

Use this skill to translate a user's causal or impact question into a CausalPy experiment choice, including plain-English questions like "did the campaign work?", "what was the effect of the rollout?", or "did the policy change sales?". See Skill triggers for additional discovery keywords. This is the design-intake skill, not the implementation skill. Optimize for agent use: follow the ordered routing steps, prefer explicit uncertainty over force-fitting, and do not write analysis code until the method route is matched or the user has answered the key ambiguity. Once the method is chosen, hand off to running-causalpy-experiments for constructor details, model configuration, priors, summaries, plots, and interpretation.

Required Intake

Before naming a method, identify these facts. If the request is missing several, ask for the single most decision-relevant missing fact.

1. Estimand: ATE, ATT, local threshold effect, event-study path, cumulative post-period impact, baseline-adjusted group contrast, or coefficient-level association.
2. Assignment mechanism: known intervention time, common pre/post group treatment, staggered adoption, cutoff, kink, instrument, observed treatment with measured confounders, or no credible assignment story.
3. Data topology: single outcome series, wide unit-by-time panel, long unit-time panel, cross-section, or single pre/post observations by unit or group.
4. Controls: none, donor units, treated/control groups, never-treated cohorts, measured covariates, near-cutoff observations, or instruments.
5. Identification support: pre-period history, donor support, overlap/positivity, no anticipation, absorbing treatment, no manipulation at cutoff, instrument validity, or baseline adjustment.
6. Backend/reporting constraints: whether the user needs OLS/sklearn, Bayesian uncertainty, effect_summary(), or a unified plot().

Routing Workflow

Use the canonical routing algorithm in Decision tree. It is deliberately written as text/pseudocode, not a visual decision tree, so agents can follow it linearly. Do not skip from a keyword such as "time series" or "panel" directly to a class; route through assignment mechanism, data topology, controls, and disqualifiers.

When a route is close but not settled, use the disambiguation cards:

• ITS vs Piecewise ITS
• ITS vs Synthetic Control vs DiD
• DiD vs Staggered DiD vs Panel Regression
• Synthetic Control vs SDiD
• IPW vs IV vs Panel Regression
• PrePostNEGD vs DiD

Output Contracts

Return exactly one of these outcomes.

Matched

• Recommended method: name one primary CausalPy experiment class.
• Why it fits: tie the recommendation to estimand, assignment mechanism, data topology, and controls.
• Disqualifiers checked: name the main alternatives rejected and why.
• Required columns/data layout: list the minimal structure needed.
• Key assumptions: state what must be credible for causal interpretation.
• Main risks: name likely failure modes and sensitivity checks.
• Next step: route to running-causalpy-experiments and the relevant method reference. If the user wants to stress-test the claim before trusting it, also suggest causal-detective.

Ambiguous

• Candidate methods: name the top two plausible CausalPy classes.
• What separates them: state the concrete distinction, such as forecast counterfactual vs segmented level/slope model.
• Deciding question: ask one question that will resolve the route.
• Next step: do not write analysis code until the user answers.

Not Identifiable Yet

• Status: the method may exist in CausalPy, but the available data or assumptions are insufficient.
• Missing requirement: name the specific missing design fact, such as no pre-period, no donor support, no overlap check, or no credible instrument.
• Deciding question: ask for the missing fact or suggest what evidence would be needed.
• Next step: do not force a method recommendation.

Not Implemented In CausalPy

• Status: state "CausalPy has not implemented the right method."
• Ideal method category: name the method family the user likely needs.
• Why no CausalPy experiment fits: explain the mismatch with assignment, estimand, treatment type, data topology, or reporting needs.
• Closest partial fit: mention a CausalPy class only if it is genuinely useful, and state its limitations.
• What would unlock CausalPy: describe the data or assumption change that would make an implemented experiment appropriate.

References

• Skill triggers
• Decision tree
• Experiment decision guide
• Method capability matrix
• When CausalPy has not implemented the right method