metacausal.aggregation.Mean¶

class metacausal.aggregation.Mean[source]¶

Bases: PointwiseStrategy

Pointwise mean aggregation.

Examples

Same four component predictions as Median’s example; here the outlier (90.0) drags the mean well above the other three models’ ~11:

>>> import numpy as np
>>> from metacausal.aggregation import Mean
>>> values = np.array([[10.0, 5.0], [11.0, 5.0], [12.0, 5.0], [90.0, 5.0]])
>>> Mean().aggregate(values)
array([30.75,  5.  ])

Methods

`__init__`
`aggregate`	Reduce a `(K, n)` component-CATE matrix to a `(n,)` ensemble CATE.

Attributes

`ensemble_weights`	Ensemble weights, if applicable.
`strategy_family`

Details

aggregate(values)[source]¶

Reduce a (K, n) component-CATE matrix to a (n,) ensemble CATE.

K is the number of component models; n is the number of evaluation points. Subclasses define the per-family rule (pointwise statistical reduction, weighted combination, learned linear combination, etc.).

PointwiseStrategy extends this contract to also accept (K,) 1-D input (returning a 0-d scalar) for the component-ATE aggregation path; see its class docstring. Other families do not meaningfully support 1-D input.

Parameters:: values (ndarray)
Return type:: ndarray