BaseExperiment

class causalpy.experiments.base.BaseExperiment

Base class for quasi experimental designs.

Subclasses should set _default_model_class to a PyMC model class (e.g. LinearRegression) so that model=None instantiates a sensible Bayesian default. To use an OLS/sklearn model, pass one explicitly.

Methods

BaseExperiment.__init__([model])
BaseExperiment.effect_summary(*[, window, ...])	Generate a decision-ready summary of causal effects.
BaseExperiment.fit(*args, **kwargs)
BaseExperiment.get_plot_data(*args, **kwargs)	Recover the data of an experiment along with the prediction and causal impact information.
BaseExperiment.get_plot_data_bayesian(*args, ...)	Return plot data for Bayesian models.
BaseExperiment.get_plot_data_ols(*args, **kwargs)	Return plot data for OLS models.
BaseExperiment.plot(*args, **kwargs)	Plot the model.
BaseExperiment.print_coefficients([round_to])	Ask the model to print its coefficients.

Attributes

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

__init__(model=None)

Parameters:

model (PyMCModel | RegressorMixin | None)

Return type:

None

classmethod __new__(*args, **kwargs)