approximate change in betas as a result of excluding ith row. Good for finding outliers / specific
        subjects that influence the model disproportionately. Good advice: don't drop these outliers, model them.

def _compute_delta_beta(self, X, T, E, weights, index=None):
        """
        approximate change in betas as a result of excluding ith row. Good for finding outliers / specific
        subjects that influence the model disproportionately. Good advice: don't drop these outliers, model them.
        """
        score_residuals = self._compute_score(X, T, E, weights, index=index)

        d = X.shape[1]
        scaled_variance_matrix = self.variance_matrix_ * np.tile(self._norm_std.values, (d, 1)).T

        delta_betas = score_residuals.dot(scaled_variance_matrix)
        delta_betas.columns = self.hazards_.index

        return delta_betas