SyntheticControl#

class causalpy.experiments.synthetic_control.SyntheticControl[source]#

The class for the synthetic control experiment.

Parameters:

  • data (DataFrame) – A pandas dataframe.

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

  • control_units (list[str]) – A list of control units to be used in the experiment.

  • treated_units (list[str]) – A list of treated units to be used in the experiment.

  • model (PyMCModel | RegressorMixin | None) – A PyMC or sklearn model. Defaults to WeightedSumFitter.

  • min_donor_correlation (float) – Minimum acceptable Pearson correlation between each control unit and treated unit in the pre-treatment period. Control units below this threshold trigger a UserWarning. Defaults to 0.0 (warn on negatively correlated donors).

  • **kwargs (Any) – Additional keyword arguments forwarded to BaseExperiment.

Notes

Estimate extraction

The model learns control-unit weights from pre-intervention outcomes and applies them to post-intervention controls to construct a synthetic untreated trajectory. Pointwise impact is the observed treated outcome minus this synthetic counterfactual, and cumulative impact is its running sum. Bayesian backends subtract the posterior conditional expectation mu rather than noisy posterior-predictive draws y_hat; OLS subtracts its weighted point prediction.

Examples

>>> import causalpy as cp
>>> df = cp.load_data("sc")
>>> treatment_time = 70
>>> seed = 42
>>> result = cp.SyntheticControl(
...     df,
...     treatment_time,
...     control_units=["a", "b", "c", "d", "e", "f", "g"],
...     treated_units=["actual"],
...     model=cp.pymc_models.WeightedSumFitter(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     ),
... )

Methods

SyntheticControl.algorithm()

Run the experiment algorithm: fit model, predict, and calculate causal impact.

SyntheticControl.effect_summary(*[, window, ...])

Generate a decision-ready summary of causal effects for Synthetic Control.

SyntheticControl.fit(*args, **kwargs)

Fit the underlying model.

SyntheticControl.generate_report(*[, ...])

Generate a self-contained HTML report for this experiment.

SyntheticControl.get_plot_data([hdi_prob, ...])

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

SyntheticControl.input_validation(data, ...)

Validate the input data and model formula for correctness.

SyntheticControl.plot(*[, round_to, ...])

Plot the synthetic control results for a specific treated unit.

SyntheticControl.print_coefficients([round_to])

Ask the model to print its coefficients.

SyntheticControl.set_maketables_options(*[, ...])

Set optional maketables rendering options for this experiment.

SyntheticControl.summary([round_to])

Print summary of main results and model coefficients.

Attributes

datapost

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

datapre

Data from before the treatment time (exclusive).

idata

Return fitted InferenceData when the model backend supports it.

supports_bayes

supports_ols

supports_pymc_forecast

labels

data

__init__(data, treatment_time, control_units, treated_units, model=None, min_donor_correlation=0.0, **kwargs)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#