PrePostFit

class causalpy.pymc_experiments.PrePostFit

A class to analyse quasi-experiments where parameter estimation is based on just the pre-intervention data.

Parameters:

• data (DataFrame) – A pandas dataframe

• treatment_time (Union[int, float, Timestamp]) – The time when treatment occured, should be in reference to the data index

• validation_time – Optional time to split the data into training and validation data sets

• formula (str) – A statistical model formula

• model – A PyMC model

Methods

PrePostFit.__init__(data, treatment_time, ...)
PrePostFit.plot([counterfactual_label, round_to])	Plot the results
PrePostFit.print_coefficients([round_to])	Prints the model coefficients
PrePostFit.summary([round_to])	Print text output summarising the results

Attributes

expt_type
idata	Access to the models InferenceData object
model

__init__(data, treatment_time, formula, validation_time=None, model=None, **kwargs)

Parameters:

• data (DataFrame)

• treatment_time (int | float | Timestamp)

• formula (str)

Return type:

None

__new__(*args, **kwargs)