causalis.data_contracts.gate_estimate

Module Contents

Classes

GateEstimate

Result contract for group-level treatment-effect estimates.

API

class causalis.data_contracts.gate_estimate.GateEstimate(/, **data: Any)

Bases: pydantic.BaseModel

Result contract for group-level treatment-effect 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: str

‘GATE’

model: str

‘IRM’

group_names: List[str]

None

values: numpy.ndarray

None

std_errors: numpy.ndarray

None

test_stats: numpy.ndarray

None

p_values: numpy.ndarray

None

ci_lower: numpy.ndarray

None

ci_upper: numpy.ndarray

None

alpha: float

None

covariance: pandas.DataFrame

None

summary_table: pandas.DataFrame

None

model_options: Dict[str, Any]

‘Field(…)’

n_group: numpy.ndarray

None

n_treated: numpy.ndarray

None

n_control: numpy.ndarray

None

share_treated: numpy.ndarray

None

mean_phi: numpy.ndarray

None

std_phi: numpy.ndarray

None

mean_propensity: numpy.ndarray

None

min_propensity: numpy.ndarray

None

max_propensity: numpy.ndarray

None

time: str

‘Field(…)’

diagnostic_data: Optional[Dict[str, Any]]

None

summary() → pandas.DataFrame

Return per-group subgroup-effect summary table.

contrast(left_group: str, right_group: str, *, alpha: Optional[float] = None, alternative: str = 'two-sided') → causalis.data_contracts.gate_contrast_estimate.GateContrastEstimate

Construct a formal post-estimation contrast between two groups.

pairwise_summary(*, reference: Optional[str] = None, alpha: Optional[float] = None, p_adjust: str = 'none') → pandas.DataFrame

Return a long-form table of formal pairwise subgroup contrasts.