DifferenceInDifferences#

class causalpy.experiments.diff_in_diff.DifferenceInDifferences[source]#

A class to analyse data from Difference in Difference settings.

Note

There is no pre/post intervention data distinction for DiD, we fit all the data available.

Parameters:

data (pd.DataFrame) – A pandas dataframe.
formula (str) – A statistical model formula.
time_variable_name (str) – Name of the data column for the time variable.
group_variable_name (str) – Name of the data column for the group variable.
post_treatment_variable_name (str, optional) – Name of the data column indicating post-treatment period. Defaults to “post_treatment”.
model (PyMCModel or RegressorMixin, optional) – A PyMC model for difference in differences. Defaults to None.

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     ),
... )

Methods

`DifferenceInDifferences.__init__`(data, ...)
`DifferenceInDifferences.effect_summary`([...])	Generate a decision-ready summary of causal effects.
`DifferenceInDifferences.fit`(args, *kwargs)
`DifferenceInDifferences.get_plot_data`(*args, ...)	Recover the data of an experiment along with the prediction and causal impact information.
`DifferenceInDifferences.get_plot_data_bayesian`(...)	Abstract method for recovering plot data.
`DifferenceInDifferences.get_plot_data_ols`(...)	Abstract method for recovering plot data.
`DifferenceInDifferences.input_validation`()
`DifferenceInDifferences.plot`(args, *kwargs)	Plot the model.
`DifferenceInDifferences.print_coefficients`([...])	Ask the model to print its coefficients.
`DifferenceInDifferences.summary`([round_to])	Print summary of main results and model coefficients.

Attributes

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

__init__(data, formula, time_variable_name, group_variable_name, post_treatment_variable_name='post_treatment', model=None, **kwargs)[source]#

Parameters:

data (DataFrame)
formula (str)
time_variable_name (str)
group_variable_name (str)
post_treatment_variable_name (str)
model (PyMCModel | RegressorMixin | None)
kwargs (dict)

Return type:

None

classmethod __new__(*args, **kwargs)#