econml/ortho_forest.py
def _pointwise_effect(self, X_single):
        """
        We need to post-process the parameters returned by the _pointwise_effect
        of the BaseOrthoForest class due to the local linear correction. The
        base class function will return the intercept and the coefficient of the
        local linear fit. We multiply it with the input co-variate to get the
        predicted effect.
        """
        parameter = super(ContinuousTreatmentOrthoForest, self)._pointwise_effect(X_single)
        X_aug = np.append([1], X_single)
        parameter = parameter.reshape((X_aug.shape[0], -1)).T
        return np.dot(parameter, X_aug)

        We need to post-process the parameters returned by the _pointwise_effect
        of the BaseOrthoForest class due to the local linear correction. The
        base class function will return the intercept and the coefficient of the
        local linear fit. We multiply it with the input co-variate to get the
        predicted effect.
        
We need to post-process the parameters returned by the _pointwise_effect
of the BaseOrthoForest class due to the local linear correction. The
base class function will return the intercept and the coefficient of the
local linear fit. We multiply it with the input co-variate to get the
predicted effect.
We need to post - process the parameters returned by the _pointwise_effect of the BaseOrthoForest class due to the local linear correction . The base class function will return the intercept and the coefficient of the local linear fit . We multiply it with the input co - variate to get the predicted effect .
def _pointwise_effect(self, X_single):
        parameter = super(ContinuousTreatmentOrthoForest, self)._pointwise_effect(X_single)
        X_aug = np.append([1], X_single)
        parameter = parameter.reshape((X_aug.shape[0], -1)).T
        return np.dot(parameter, X_aug)
def _pointwise_effect ( self , X_single ) : parameter = super ( ContinuousTreatmentOrthoForest , self ) . _pointwise_effect ( X_single ) X_aug = np . append ( [ 1 ] , X_single ) parameter = parameter . reshape ( ( X_aug . shape [ 0 ] , - 1 ) ) . T return np . dot ( parameter , X_aug )
We need to post-process the parameters returned by the _pointwise_effect
of the BaseOrthoForest class due to the local linear correction.
We need to post - process the parameters returned by the _pointwise_effect of the BaseOrthoForest class due to the local linear correction .
"""
        We need to post-process the parameters returned by the _pointwise_effect
        of the BaseOrthoForest class due to the local linear correction. The
        base class function will return the intercept and the coefficient of the
        local linear fit. We multiply it with the input co-variate to get the
        predicted effect.
        """
self
X_single