text: import causalicp as icp
  import sempler
  import sempler.generators
  
  # Generate a random graph and construct a linear-Gaussian SCM
  W = sempler.generators.dag_avg_deg(4, 2.5, 0.5, 2)
  scm = sempler.LGANM(W, (-1, 1), (1, 2))
  
  # Generate a sample for setting 1: Observational setting
  data = [scm.sample(n=100)]
  
  # Setting 2: Shift-intervention on X1
  data += [scm.sample(n=130, shift_interventions={1: (3.1, 5.4)})]
  
  # Setting 3: Do-intervention on X2
  data += [scm.sample(n=98, do_interventions={2: (-1, 3)})]
  
  
  result = icp.fit(data, 3, alpha=0.05, precompute=True, verbose=True, color=False)
  