# `causalis.data_contracts.iv_causal_data`

Causalis dataclass for storing cross-sectional DataFrame and column metadata for instrumental variables causal inference.

## Module Contents

### Classes

`IVCausalData` | Container for instrumental variables causal inference datasets.

### API

class causalis.data_contracts.iv_causal_data.IVCausalData(/, **data: Any)

  Bases: `causalis.data_contracts.causaldata.CausalData`

  Container for instrumental variables causal inference datasets.

  Extends :class:`CausalData` with exactly one instrument column. The stored DataFrame is restricted to outcome, treatment, instrument, confounder, and optional user_id columns.

  Attributes

  df : pd.DataFrame DataFrame restricted to the columns used by the IV analysis. treatment_name : str Column name representing the endogenous treatment variable. outcome_name : str Column name representing the outcome variable. instruments_names : List[str] Name of the instrument column, stored as a single-item list. confounders_names : List[str] Names of the confounder columns (may be empty). user_id_name : str, optional Column name representing the unique identifier for each observation/user.

  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.

  instruments_names: List[str]

    ‘Field(…)’

  classmethod from_df(df: pandas.DataFrame, treatment: str, outcome: str, instruments: Union[str, List[str]], confounders: Optional[Union[str, List[str]]] = None, user_id: Optional[str] = None, **kwargs: Any) → causalis.data_contracts.iv_causal_data.IVCausalData

    Friendly constructor for IVCausalData.

    Parameters

    df : pd.DataFrame The DataFrame containing the data. treatment : str Column name representing the endogenous treatment variable. outcome : str Column name representing the outcome variable. instruments : Union[str, List[str]] Column name(s) representing the instrumental variable(s). confounders : Union[str, List[str]], optional Column name(s) representing the observed confounders/covariates. user_id : str, optional Column name representing the unique identifier for each observation/user. **kwargs : Any Additional arguments passed to the Pydantic model constructor.

    Returns

    IVCausalData A validated IVCausalData instance.

  property instruments: List[str]

    List of instrument column names.

    Returns

    List[str] Names of the instrument columns.

  property Z: pandas.DataFrame

    Design matrix of instruments.

    Returns

    pd.DataFrame The DataFrame containing only instrument columns.

  get_df(columns: Optional[List[str]] = None, include_treatment: bool = True, include_outcome: bool = True, include_confounders: bool = True, include_user_id: bool = False, include_instruments: bool = True) → pandas.DataFrame

    Get a DataFrame with specified columns.

    Parameters

    columns : List[str], optional Specific column names to include. include_treatment : bool, default True Whether to include the treatment column. include_outcome : bool, default True Whether to include the outcome column. include_confounders : bool, default True Whether to include confounder columns. include_user_id : bool, default False Whether to include the user_id column. include_instruments : bool, default True Whether to include instrument columns.

    Returns

    pd.DataFrame A copy of the internal DataFrame with selected columns.

    Raises

    ValueError If any specified columns do not exist.

  __repr__() → str