causalis.data_contracts.panel_estimate

Module Contents

Classes

PanelEstimate

Result contract for dynamic synthetic-control effect-path estimates.

API

class causalis.data_contracts.panel_estimate.PanelEstimate(/, **data: Any)

Bases: pydantic.BaseModel

Result contract for dynamic synthetic-control effect-path estimates.

Initialization

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config

‘ConfigDict(…)’

estimand: Literal[dynamic_effect_path]

‘dynamic_effect_path’

model: str

None

treated_unit: Hashable

None

treatment_start: causalis.data_contracts.panel_data_scm.TimeLike

None

pre_times: List[causalis.data_contracts.panel_data_scm.TimeLike]

None

post_times: List[causalis.data_contracts.panel_data_scm.TimeLike]

None

effect_by_time: pandas.Series

None

ci_lower_by_time: pandas.Series

None

ci_upper_by_time: pandas.Series

None

p_value_by_time: pandas.Series

None

is_significant_by_time: pandas.Series

None

confidence_set_by_time: Dict[causalis.data_contracts.panel_data_scm.TimeLike, list[tuple[float, float]]]

None

alpha: float

None

observed_outcome: pandas.Series

None

synthetic_outcome: pandas.Series

None

donor_weights_augmented: Dict[Hashable, float]

None

diagnostics: Dict[str, Any]

‘Field(…)’

created_at: datetime.datetime

‘Field(…)’

summary() → pandas.DataFrame

Return a compact CausalEstimate-style summary table.

summary_poinwise() → pandas.DataFrame

Return pointwise post-period estimates as a flat DataFrame.