fix

Signed-off-by: Rahul Shrestha <rahulshrestha0101@gmail.com>

tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py

@@ -44,54 +44,6 @@ def intervene(self, treatment_value, sample_size=100):
 
 @mark.usefixtures("fixed_seed")
 class TestCausalInferenceDiscoveryBook(object):
-    def test_dowhy_chapter_6(self):
-        # Instantiate the SCM
-        scm = GPSMemorySCM()
-
-        # Generate observational data
-        gps_obs, hippocampus_obs, memory_obs = scm.sample(600)
-
-        # Run an experiment
-        treatments = []
-        experiment_results = []
-
-        # Sample over various treatments
-        for treatment in np.arange(1, 21):
-            gps_hours, hippocampus, memory = scm.intervene(treatment_value=treatment, sample_size=30)
-            experiment_results.append(memory)
-            treatments.append(gps_hours)
-
-        # Naive model 1
-
-        lr_naive = LinearRegression()
-        lr_naive.fit(X=gps_obs.reshape(-1, 1), y=memory_obs)
-
-        # Experimental model
-
-        treatments_unpack = np.array(treatments).flatten()
-        results_unpack = np.array(experiment_results).flatten()
-
-        lr_experiment = LinearRegression()
-        lr_experiment.fit(X=treatments_unpack.reshape(-1, 1), y=results_unpack)
-
-        X_test = np.arange(1, 21).reshape(-1, 1)
-        preds_naive = lr_naive.predict(X_test)
-        preds_experiment = lr_experiment.predict(X_test)
-
-        # Get coefficients
-        print(f"Naive model:\n{lr_naive.coef_[0]}\n")
-        print(f"Experiemntal model:\n{lr_experiment.coef_[0]}")
-
-        # Model E(Z|X)
-        lr_zx = LinearRegression()
-        lr_zx.fit(X=gps_obs.reshape(-1, 1), y=hippocampus_obs)
-
-        # Model E(Y|X, Z)E(X)
-        lr_yxz = LinearRegression()
-        lr_yxz.fit(X=np.array([gps_obs, hippocampus_obs]).T, y=memory_obs)
-
-        # Compute the expected causal effect
-        lr_zx.coef_[0] * lr_yxz.coef_[1]
 
     def test_dowhy_chapter_7(self):
         # Instantiate the SCM