PipelineContext#

class causalpy.pipeline.PipelineContext[source]#

Mutable container that accumulates results as pipeline steps execute.

Each step reads from and writes to this context, building up a complete record of the analysis.

data#

The input dataset.

Type:: pd.DataFrame

experiment#

The fitted experiment object, populated by EstimateEffect.

Type:: BaseExperiment or None

experiment_config#

The configuration used to create the experiment (method class + keyword arguments), so that downstream steps like SensitivityAnalysis can derive experiment factories.

Type:: dict or None

effect_summary#

The effect summary from the primary experiment.

Type:: EffectSummary or None

sensitivity_results#

Accumulated sensitivity / diagnostic check results.

Type:: list

report#

Generated report artifact, populated by GenerateReport.

Type:: object or None

Methods

PipelineContext.__init__(data[, experiment, ...])

Attributes

`effect_summary`
`experiment`
`experiment_config`
`report`
`data`
`sensitivity_results`

__init__(data, experiment=None, experiment_config=None, effect_summary=None, sensitivity_results=<factory>, report=None)#

Parameters:

data (DataFrame)
experiment (BaseExperiment | None)
experiment_config (dict[str, Any] | None)
effect_summary (EffectSummary | None)
sensitivity_results (list[Any])
report (Any)

Return type:

None

classmethod __new__(*args, **kwargs)#