Mention estimation approach in doc string.

metalearners/rlearner.py

@@ -536,6 +536,30 @@ def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
     def predict_conditional_average_outcomes(
         self, X: Matrix, is_oos: bool, oos_method: OosMethod = OVERALL
     ) -> np.ndarray:
+        r"""Predict the vectors of conditional average outcomes.
+
+        These are defined as :math:`\mathbb{E}[Y_i(w) | X]` for each treatment variant
+        :math:`w`.
+
+        If ``is_oos``, an acronym for 'is out of sample' is ``False``,
+        the estimates will stem from cross-fitting. Otherwise,
+        various approaches exist, specified via ``oos_method``.
+
+        The returned ndarray is of shape:
+
+        * :math:`(n_{obs}, n_{variants}, 1)` if the outcome is a scalar, i.e. in case
+          of a regression problem.
+
+        * :math:`(n_{obs}, n_{variants}, n_{classes})` if the outcome is a class,
+          i.e. in case of a classification problem.
+
+        The conditional average outcomes are estimated as follows:
+
+        * :math:`Y_i(0) = \hat{\mu}(X_i) - \sum_{k=1}^{K} \hat{e}_k(X_i) \hat{\tau_k}(X_i)`
+        * :math:`Y_i(k) = Y_i(0) + \hat{\tau_k}(X_i)` for :math:`k \in \{1, \dots, K\}`
+
+        where :math:`K` is the number of treatment variants.
+        """
         n_obs = len(X)
 
         cate_estimates = self.predict(