SyntheticDifferenceInDifferences#

class causalpy.experiments.synthetic_difference_in_differences.SyntheticDifferenceInDifferences[source]#

Bayesian Synthetic Difference-in-Differences experiment.

Combines the synthetic control method’s unit weighting with difference-in-differences time weighting. The treatment effect (tau) is computed analytically from the posterior weight distributions via the double-difference formula, rather than being estimated inside the MCMC model (cut-posterior formulation).

Parameters:

  • data (DataFrame) – A dataframe in wide format (columns = units, rows = time periods).

  • treatment_time (int | float | Timestamp) – The time when treatment occurred, should be in reference to the data index.

  • control_units (list[str]) – A list of control unit column names.

  • treated_units (list[str]) – A list of treated unit column names.

  • model (PyMCModel | RegressorMixin | None) – A SyntheticDifferenceInDifferencesWeightFitter instance. Defaults to SyntheticDifferenceInDifferencesWeightFitter.

Examples

>>> import causalpy as cp
>>> df = cp.load_data("sc")
>>> treatment_time = 70
>>> result = cp.SyntheticDifferenceInDifferences(
...     df,
...     treatment_time,
...     control_units=["a", "b", "c", "d", "e", "f", "g"],
...     treated_units=["actual"],
...     model=cp.pymc_models.SyntheticDifferenceInDifferencesWeightFitter(
...         sample_kwargs={
...             "tune": 20,
...             "draws": 20,
...             "chains": 2,
...             "cores": 2,
...             "progressbar": False,
...         }
...     ),
... )

Notes

This implements Bayesian SDiD method. The model fits two weight modules via MCMC:

  • Unit weights (omega): balance control units against treated units in the pre-treatment period, similar to synthetic control.

  • Time weights (lambda): balance pre-treatment periods against post-treatment periods for control units.

The treatment effect is then computed analytically via the double-difference:

\[\tau = \bar{\Delta}_{\text{post}} - \boldsymbol{\lambda}^\top \boldsymbol{\Delta}_{\text{pre}}\]

where \(\Delta_t = y_{\text{tr},t} - (\omega_0 + \boldsymbol{\omega}^\top \mathbf{Y}_{\text{co},t})\) is the gap between the observed treated outcome and the synthetic control at time t.

References

Methods

SyntheticDifferenceInDifferences.algorithm()

Run the SDiD algorithm: fit weight modules, compute tau analytically.

SyntheticDifferenceInDifferences.effect_summary(*)

Generate a decision-ready summary of causal effects for SDiD.

SyntheticDifferenceInDifferences.fit(*args, ...)

SyntheticDifferenceInDifferences.generate_report(*)

Generate a self-contained HTML report for this experiment.

SyntheticDifferenceInDifferences.get_plot_data(...)

Recover the data of an experiment along with the prediction and causal impact information.

SyntheticDifferenceInDifferences.get_plot_data_bayesian(...)

Return plot data for Bayesian models.

SyntheticDifferenceInDifferences.get_plot_data_ols(...)

Return plot data for OLS models.

SyntheticDifferenceInDifferences.input_validation(...)

Validate the input data for correctness.

SyntheticDifferenceInDifferences.plot(*args)

Plot the model.

SyntheticDifferenceInDifferences.print_coefficients([...])

Ask the model to print its coefficients.

SyntheticDifferenceInDifferences.set_maketables_options(*)

Set optional maketables rendering options for this experiment.

SyntheticDifferenceInDifferences.summary([...])

Print summary of main results.

Attributes

datapost

Data from on or after the treatment time (inclusive).

datapre

Data from before the treatment time (exclusive).

idata

Return the InferenceData object of the model.

supports_bayes

supports_ols

labels

data

__init__(data, treatment_time, control_units, treated_units, model=None, **kwargs)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#