causalis.data_contracts.multicausal_estimate

Module Contents

Classes

MultiCausalEstimate

Result container for causal effect estimates.

API

class causalis.data_contracts.multicausal_estimate.MultiCausalEstimate(/, **data: Any)

Bases: pydantic.BaseModel

Result container for causal effect estimates.

Parameters

estimand : str The estimand being estimated (e.g., ‘ATE’, ‘ATTE’, ‘CATE’). model : str The name of the model used for estimation. model_options : dict Options passed to the model. value : float The estimated absolute effect. ci_upper_absolute : float Upper bound of the absolute confidence interval. ci_lower_absolute : float Lower bound of the absolute confidence interval. value_relative : float, optional The estimated relative effect. ci_upper_relative : float, optional Upper bound of the relative confidence interval. ci_lower_relative : float, optional Lower bound of the relative confidence interval. alpha : float The significance level (e.g., 0.05). p_value : float, optional The p-value from the test. is_significant : bool Whether the result is statistically significant at alpha. n_treated : int Number of units in the treatment group. n_control : int Number of units in the control group. outcome : str The name of the outcome variable. treatment : str The name of the treatment variable. confounders : list of str, optional The names of the confounders used in the model. time : str The date when the estimate was created (YYYY-MM-DD). diagnostic_data : DiagnosticData, optional Additional diagnostic data_contracts.

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

None

model: str

None

model_options: Dict[str, Any]

‘Field(…)’

value: numpy.ndarray

None

ci_upper_absolute: numpy.ndarray

None

ci_lower_absolute: numpy.ndarray

None

value_relative: Optional[numpy.ndarray]

None

ci_upper_relative: Optional[numpy.ndarray]

None

ci_lower_relative: Optional[numpy.ndarray]

None

alpha: float

None

p_value: Optional[numpy.ndarray]

None

is_significant: List[bool]

None

n_treated: int

None

n_control: int

None

outcome: str

None

treatment: List[str]

None

n_treated_by_arm: Optional[numpy.ndarray]

None

treatment_mean: Optional[numpy.ndarray]

None

control_mean: Optional[float]

None

control_mean_by_arm: Optional[numpy.ndarray]

None

contrast_labels: List[str]

‘Field(…)’

confounders: List[str]

‘Field(…)’

time: str

‘Field(…)’

diagnostic_data: Optional[causalis.data_contracts.causal_diagnostic_data.DiagnosticData]

None

summary() → pandas.DataFrame

Return a CausalEstimate-like summary for all baseline contrasts.

Returns

pd.DataFrame Summary indexed by field and with one column per contrast (d_k vs d_0).