4.1.7. doubleml.utils.PSProcessorConfig#

class doubleml.utils.PSProcessorConfig(clipping_threshold: float = 0.01, extreme_threshold: float = 1e-12, calibration_method: str | None = None, cv_calibration: bool = False)#

Configuration for propensity score processing.

This dataclass holds the configuration parameters used by PSProcessor for propensity score calibration, clipping, and validation.

Parameters:

clipping_threshold (float, default=1e-2) – Minimum and maximum bound for propensity scores after clipping. Must be between 0 and 0.5.
extreme_threshold (float, default=1e-12) – Threshold below which propensity scores are considered extreme. Propensity scores are clipped based on this value when scores are too close to 0 or 1 to avoid numerical instability. Must be between 0 and 0.5.
calibration_method ({'isotonic', None}, optional) – If provided, applies the specified calibration method to the propensity scores before clipping. Currently supports: - ‘isotonic’: Isotonic regression calibration - None: No calibration applied
cv_calibration (bool, default=False) – Whether to use cross-validation for calibration. Only applies if a calibration method is specified. Requires calibration_method to be set.

Examples

>>> from doubleml.utils import PSProcessorConfig, PSProcessor
>>> config = PSProcessorConfig(
...     clipping_threshold=0.05,
...     calibration_method='isotonic',
...     cv_calibration=True
... )
>>> processor = PSProcessor.from_config(config)

Methods

Attributes

`calibration_method`
`clipping_threshold`
`cv_calibration`
`extreme_threshold`