import polars as pl
import pandas as pd
from typing import Union, List, Optional
import io

def impact_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    outcome_col: str,
    treatment_col: str,
    covariates: Optional[List[str]] = None,
    method: str = "diff_in_diff"
) -> pl.DataFrame:
    """
    Impact Analysis of Interventions
    ================================

    What
    ----
    Estimate intervention effects (e.g., before/after, treatment/control).

    When
    ----
    Program evaluation or pilot impact.

    Why
    ---
    Understand what worked and by how much.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    outcome_col : str
        Outcome variable.
    treatment_col : str
        Indicator of treated vs control units.
    covariates : list[str] | None
        Optional confounders for adjustment.
    method : str, default "diff_in_diff"
        Estimation approach.

    Returns
    -------
    pl.DataFrame
        Placeholder effect estimate.

    Example
    -------
    >>> impact_analysis(df, outcome_col="income", treatment_col="program")
    """
    if isinstance(data, str):
        df = pl.read_csv(data)
    elif isinstance(data, io.BytesIO):
        df = pl.read_csv(data)
    elif isinstance(data, pd.DataFrame):
        df = pl.from_pandas(data)
    elif isinstance(data, pl.DataFrame):
        df = data
    else:
        raise TypeError("Unsupported data type")

    return pl.DataFrame({"method": [method], "effect": [0.0]})
