InterruptedTimeSeries#

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

A wrapper around PrePostFit class

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
formula (str) – A statistical model formula
model – A PyMC model

Example

>>> import causalpy as cp
>>> df = (
...     cp.load_data("its")
...     .assign(date=lambda x: pd.to_datetime(x["date"]))
...     .set_index("date")
... )
>>> treatment_time = pd.to_datetime("2017-01-01")
>>> seed = 42
>>> result = cp.InterruptedTimeSeries(
...     df,
...     treatment_time,
...     formula="y ~ 1 + t + C(month)",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     )
... )

Methods

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

Attributes

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

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

Parameters:

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

Return type:

None

__new__(**kwargs)#