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](reference/triggers.md) 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](reference/decision_tree.md). 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](reference/disambiguation/its_vs_piecewise_its.md) - [ITS vs Synthetic Control vs DiD](reference/disambiguation/its_vs_sc_vs_did.md) - [DiD vs Staggered DiD vs Panel Regression](reference/disambiguation/did_vs_staggered_vs_panel.md) - [Synthetic Control vs SDiD](reference/disambiguation/sc_vs_sdid.md) - [IPW vs IV vs Panel Regression](reference/disambiguation/ipw_vs_iv_vs_panel.md) - [PrePostNEGD vs DiD](reference/disambiguation/prepostnegd_vs_did.md) ## 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](reference/triggers.md) - [Decision tree](reference/decision_tree.md) - [Experiment decision guide](reference/experiment_decision_guide.md) - [Method capability matrix](reference/method_capability_matrix.md) - [When CausalPy has not implemented the right method](reference/not_in_causalpy.md)