code: def _compute_delta_beta(self, X, T, E, weights, index=None):
          
          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
docs: 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.