PrePostFit#

class causalpy.experiments.prepostfit.PrePostFit[source]#

A base class for quasi-experimental designs where parameter estimation is based on just pre-intervention data. This class is not directly invoked by the user.

Methods

`PrePostFit.__init__`(data, treatment_time, ...)
`PrePostFit.bayesian_plot`([round_to])	Plot the results
`PrePostFit.input_validation`(data, treatment_time)	Validate the input data and model formula for correctness
`PrePostFit.ols_plot`([round_to])	Plot the results
`PrePostFit.plot`(args, *kwargs)	Plot the model.
`PrePostFit.print_coefficients`([round_to])	Ask the model to print its coefficients.
`PrePostFit.summary`([round_to])	Print summary of main results and model coefficients.

Attributes

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

__init__(data, treatment_time, formula, model=None, **kwargs)[source]#

Parameters:

data (DataFrame)
treatment_time (int | float | Timestamp)
formula (str)

Return type:

None

__new__(**kwargs)#