libuplift.metrics.regression
============================

.. py:module:: libuplift.metrics.regression

.. autoapi-nested-parse::

   Metrics to assess performance of uplift regression task

   Functions named as ``*_score`` return a scalar value to maximize: the higher
   the better

   Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
   the lower the better

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Functions
---------

.. autoapisummary::

   libuplift.metrics.regression.e_sate
   libuplift.metrics.regression.e_satt
   libuplift.metrics.regression.sim_gain_at
   libuplift.metrics.regression.sim_gain


Module Contents
---------------

.. py:function:: e_sate(y_true, y_pred, trt, n_trt=1)

   
   Absolute error on Sample Average Treatment Effect.

   Works by computing ATE using model predictions and true outcomes.
   Absolute value of the difference between the two values is returned.

   For multiple treatments, return weighted average.  This measure is
   not very effective.















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.. py:function:: e_satt(y_true, y_pred, trt, n_trt=1)

   
   Absolute error on Sample Average Treatment Effect on the Treated.

   Works by computing ATT using model predictions and true outcomes.
   Absolute value of the difference between the two values is returned.

   For multiple treatments, return weighted average.  This measure is
   not very effective.















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.. py:function:: sim_gain_at(y_true, y_pred, trt, n_trt=None, *, quantile=0.5)

   
   Simulated on-policy gains at given quantile of targeted
   population.

   For mulitple treatments, the most beneficial treatment of each
   case is selected.  Enough data must be present in all treatments
   for the measure to be well defined.















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.. py:function:: sim_gain(y_true, y_pred, trt, n_trt=None, *, quantile=0.5)

   
   Simulated on-policy gains for the whole population.
















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