code: def test_pearson_effect():
      df = pd.DataFrame(
          dict(
              t=[1, 1, 1, 2, 2, 2, 3, 3, 3],
              y=[1, 1, 1, 2, 3, 4, 3, 5, 7],
          )
      )
  
      result = pearson_effect(
          df, treatment_column="t", outcome_column="y"
      )
      assert round(result, 3) == 0.840
  
file_path: fklearn/tests/causal/test_effects.py
imports: import pandas
  import numpy
  from fklearn.causal.effects import linear_effect, spearman_effect, pearson_effect, exponential_coefficient_effect, logistic_coefficient_effect
label_desc: Use image node_id 3 for calling a global function with example usage: test_pearson_effect() without return types