running-causalpy-experiments

name: running-causalpy-experiments description: Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.

Running CausalPy Experiments

Use this skill when the CausalPy experiment class is already known or has just been selected by choosing-causalpy-methods. This skill is for execution: preparing data, instantiating the experiment, choosing a model backend, setting sane priors, inspecting outputs, plotting, and communicating results.

Workflow

Load and validate a pandas DataFrame with the data layout required by the chosen experiment.
Choose a backend: PyMC models for posterior uncertainty and priors, or sklearn-compatible regressors where the experiment supports OLS/sklearn.
Configure the model before construction. For PyMC, set sample_kwargs and scale-aware priors when predictors or outcomes are not standardized.
Instantiate the experiment. CausalPy experiments fit during initialization.
Inspect outputs with summary(), effect_summary(), print_coefficients(), and plot() only where the chosen experiment supports them.
Run relevant sensitivity checks through cp.Pipeline, cp.EstimateEffect, and cp.SensitivityAnalysis when robustness matters.

Model And Prior Guardrails

Do not blindly accept diffuse default priors when predictors and outcomes are on very different scales. Either standardize the modeling variables or pass scale-aware priors to the PyMC model.
For cp.pymc_models.LinearRegression, configure priors for beta and the observation noise inside y_hat.
For synthetic-control weight models, priors control donor-weight regularization and outcome noise; see WeightedSumFitter, SoftmaxWeightedSumFitter, and SyntheticDifferenceInDifferencesWeightFitter.
For PropensityScore, standardize continuous confounders or use coefficient priors that imply plausible log-odds shifts.
For InstrumentalVariableRegression, priors are passed at the experiment level through priors=... and should reflect the scale of both the treatment-stage and outcome-stage regressions.
Always check posterior diagnostics, prior predictive plausibility when available, coefficient magnitudes, counterfactual fit in the pre-period, and whether effect summaries are stable under reasonable prior alternatives.

Common Output Methods

experiment.summary(): Prints a method-specific summary where implemented.
experiment.effect_summary(): Returns a decision-ready structured effect summary where implemented.
experiment.plot(): Visualizes fitted values, counterfactuals, effects, or diagnostics where implemented.
experiment.print_coefficients(): Shows model coefficients for model-backed experiments.
result = cp.Pipeline(...).run(): Runs estimation, sensitivity checks, and report generation as a reproducible workflow.

Important Exceptions

InversePropensityWeighting.plot() is intentionally a stub. Use plot_ate() and plot_balance_ecdf() instead.
InversePropensityWeighting.effect_summary() is not implemented. Inspect ATE draws, overlap, balance, and weight stability instead.
InstrumentalVariable.plot(), summary(), and effect_summary() are not implemented, so inspect model outputs and first-stage/second-stage diagnostics directly.
PanelRegression.effect_summary() is not implemented because panel fixed-effects models report coefficient-level estimates rather than time-window impacts. Use summary(), print_coefficients(), and plot() or plot_coefficients().