PanelRegression

class causalpy.experiments.panel_regression.PanelRegression

Panel fixed effects regression with optional within transformation.

Methods

PanelRegression.__init__(data, formula, ...)
PanelRegression.effect_summary(**kwargs)	Generate a decision-ready summary of causal effects.
PanelRegression.fit(*args, **kwargs)
PanelRegression.get_plot_data(*args, **kwargs)	Recover the data of an experiment along with the prediction and causal impact information.
PanelRegression.get_plot_data_bayesian([...])	Return data with fitted values, residuals, and prediction HDI.
PanelRegression.get_plot_data_ols()	Return data with fitted values and residuals for OLS models.
PanelRegression.input_validation()
PanelRegression.plot(*args, **kwargs)	Plot the model.
PanelRegression.plot_coefficients([...])	Forest plot of covariate coefficients excluding FE coefficients.
PanelRegression.plot_residuals([kind, by])	Residual diagnostic plots.
PanelRegression.plot_trajectories([units, ...])	Plot observed vs predicted trajectories for selected units.
PanelRegression.plot_unit_effects([...])	Plot distribution of unit fixed effects (dummies only).
PanelRegression.print_coefficients([round_to])	Ask the model to print its coefficients.
PanelRegression.summary([round_to])	Print summary of panel dimensions and coefficients.

Attributes

idata	Return the InferenceData object of the model.
supports_bayes
supports_ols
labels

__init__(data, formula, unit_fe_variable, time_fe_variable=None, fe_method='dummies', model=None, **kwargs)

Parameters:

• data (DataFrame)

• formula (str)

• unit_fe_variable (str)

• time_fe_variable (str | None)

• fe_method (Literal['dummies', 'within'])

• model (PyMCModel | RegressorMixin | None)

• kwargs (dict)

Return type:

None

classmethod __new__(*args, **kwargs)