EstimateEffect#

class causalpy.steps.estimate_effect.EstimateEffect[source]#

Pipeline step that fits a causal experiment.

Captures the experiment class and its keyword arguments. When the pipeline runs, instantiates the experiment with the pipeline’s data (which triggers fitting) and stores the result in the context.

Parameters:

  • method (type[BaseExperiment]) – The experiment class to instantiate (e.g. cp.InterruptedTimeSeries).

  • **kwargs (Any) – Keyword arguments accepted by method’s constructor, except data, which the pipeline supplies. This is a deliberately narrow dynamic forwarder: method may be an integrator-provided BaseExperiment subclass, so its accepted constructor keys cannot be enumerated here. Built-in experiment constructors declare every supported key explicitly; unsupported, misspelled, or incomplete arguments raise TypeError during pipeline validation.

Examples

>>> import causalpy as cp
>>> step = cp.EstimateEffect(
...     method=cp.InterruptedTimeSeries,
...     treatment_time=pd.Timestamp("2020-01-01"),
...     formula="y ~ 1 + t",
...     model=cp.pymc_models.LinearRegression(),
... )

Methods

EstimateEffect.run(context)

Instantiate and fit the experiment.

EstimateEffect.validate(context)

Check that the step is properly configured.
__init__(method, **kwargs)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#