econml.validate.EvaluationResults

Results class for combination of all tests.

Parameters:

cal_res (CalibrationEvaluationResults object) – Results object for calibration test
blp_res (BLPEvaluationResults object) – Results object for BLP test
qini_res (UpliftEvaluationResults object) – Results object for QINI test
toc_res (UpliftEvaluationResults object) – Results object for TOC test

__init__(cal_res: CalibrationEvaluationResults, blp_res: BLPEvaluationResults, qini_res: UpliftEvaluationResults, toc_res: UpliftEvaluationResults)[source]

Methods

`__init__`(cal_res, blp_res, qini_res, toc_res)
`plot_cal`(tmt)	Plot group average treatment effects (GATEs) and predicted GATEs by quantile-based group in validation sample.
`plot_qini`(tmt[, err_type])	Plot QINI curves.
`plot_toc`(tmt[, err_type])	Plot TOC curves.
`summary`()	Construct a dataframe summarizing the results of all 3 tests.

plot_cal(tmt: int)[source]

Plot group average treatment effects (GATEs) and predicted GATEs by quantile-based group in validation sample.

Parameters:: tmt (integer) – Treatment level to plot
Return type:: matplotlib plot with predicted GATE on x-axis and GATE (and 95% CI) on y-axis

plot_qini(tmt: int, err_type: str = None)[source]

Plot QINI curves.

Parameters:

tmt (integer) – Treatment level 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 QINI value (and 95% CI) on y-axis

plot_toc(tmt: int, err_type: str = None)[source]

Plot TOC curves.

Parameters:

tmt (integer) – Treatment level 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 TOC value (and 95% CI) on y-axis

Construct a dataframe summarizing the results of all 3 tests.