econml.validate.UpliftEvaluationResults
- class econml.validate.UpliftEvaluationResults(params: List[float], errs: List[float], pvals: List[float], treatments: numpy.array, curve_data_dict: Dict[Any, pandas.core.frame.DataFrame])[source]
Bases:
objectResults class for uplift curve-based tests.
- Parameters
params (list or numpy array of floats) – Sequence of estimated QINI coefficient values
errs (list or numpy array of floats) – Sequence of estimated QINI coefficient standard errors
pvals (list or numpy array of floats) – Sequence of estimated QINI coefficient p-values
treatments (list or numpy array of floats) – Sequence of treatment labels
curve_data_dict (dict) – Dictionary mapping treatment levels to dataframes containing necessary data for plotting uplift curves
- __init__(params: List[float], errs: List[float], pvals: List[float], treatments: numpy.array, curve_data_dict: Dict[Any, pandas.core.frame.DataFrame])[source]
Methods
__init__(params, errs, pvals, treatments, ...)plot_uplift(tmt[, err_type])Plots uplift curves.
summary()Constructs dataframe summarizing the results of the QINI test.
- plot_uplift(tmt: Any, err_type: Optional[str] = None)[source]
Plots uplift curves.
- Parameters
tmt (any (sortable)) – Name of treatment to plot.
err_type (str) – Type of error to plot. Accepted values are normal (None), two-sided uniform confidence band (‘ucb2’), or 1-sided uniform confidence band (‘ucb1’).
- Return type
matplotlib plot with percentage treated on x-axis and uplift metric (and 95% CI) on y-axis