# `causalis.data_contracts.iv_causal_estimate`

## Module Contents

### Classes

`IVCausalEstimate` | Result container for instrumental-variable causal effect estimates.

### API

class causalis.data_contracts.iv_causal_estimate.IVCausalEstimate(/, **data: Any)

  Bases: `pydantic.BaseModel`

  Result container for instrumental-variable causal 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

    None

  model: str

    None

  model_options: Dict[str, Any]

    ‘Field(…)’

  value: float

    None

  std_error: float

    None

  t_stat: float

    None

  p_value: Optional[float]

    None

  ci_lower_absolute: float

    None

  ci_upper_absolute: float

    None

  value_relative: Optional[float]

    None

  ci_lower_relative: Optional[float]

    None

  ci_upper_relative: Optional[float]

    None

  alpha: float

    None

  is_significant: bool

    None

  outcome: str

    None

  treatment: str

    None

  instrument: str

    None

  confounders: List[str]

    ‘Field(…)’

  time: str

    ‘Field(…)’

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

    None

  summary() → pandas.DataFrame

    Return a vertical summary DataFrame of the results.