Running Causalpy Experiments

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

1. Load and validate a pandas DataFrame with the data layout required by the chosen experiment.
2. Choose a backend: PyMC models for posterior uncertainty and priors, or sklearn-compatible regressors where the experiment supports OLS/sklearn.
3. Configure the model before construction. For PyMC, set sample_kwargs and scale-aware priors when predictors or outcomes are not standardized.
4. Instantiate the experiment. CausalPy experiments fit during initialization.
5. Inspect outputs with summary(), effect_summary(), print_coefficients(), and plot() only where the chosen experiment supports them.
6. 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().

References

• Scale-aware custom priors
• Difference-in-Differences
• Interrupted Time Series
• Piecewise Interrupted Time Series
• Synthetic Control
• Synthetic Difference-in-Differences
• Panel Regression
• PrePostNEGD
• Regression Discontinuity
• Regression Kink
• Staggered Difference-in-Differences
• Instrumental Variable
• Inverse Propensity Weighting

Source: pymc-labs/CausalPy — distributed by TomeVault.