Replace list+ndarray concatenation with numpy operations in tutorials (#91)

* Initial plan

* Replace list+ndarray with np.insert for clearer numpy operations in tutorials

Co-authored-by: TomeHirata <33407409+TomeHirata@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: TomeHirata <33407409+TomeHirata@users.noreply.github.com>

Author: Copilot

docs/source/tutorials/hillstrom.rst

@@ -142,15 +142,15 @@ Let's also examine how each campaign affects spending in specific intervals usin
     pte_women_ctrl, pte_lower_women_ctrl, pte_upper_women_ctrl = simple_estimator.predict_pte(
         target_treatment_arm=2,  # Women's email
         control_treatment_arm=0,  # No email control
-        locations=[-1] + revenue_locations,
+        locations=np.insert(revenue_locations, 0, -1),
         variance_type="moment"
     )
 
     # Compute PTE: Men's email vs Control
     pte_men_ctrl, pte_lower_men_ctrl, pte_upper_men_ctrl = simple_estimator.predict_pte(
         target_treatment_arm=1,  # Men's email
         control_treatment_arm=0,  # No email control
-        locations=[-1] + revenue_locations,
+        locations=np.insert(revenue_locations, 0, -1),
         variance_type="moment"
     )
 
@@ -272,14 +272,14 @@ Revenue Category Analysis with PTE
     pte_simple, pte_lower_simple, pte_upper_simple = simple_estimator.predict_pte(
         target_treatment_arm=1,  # Women's email
         control_treatment_arm=0,  # Men's email
-        locations=[-1] + revenue_locations,
+        locations=np.insert(revenue_locations, 0, -1),
         variance_type="moment"
     )
 
     pte_ml, pte_lower_ml, pte_upper_ml = ml_estimator.predict_pte(
         target_treatment_arm=1,  # Women's email
         control_treatment_arm=0,  # Men's email
-        locations=[-1] + revenue_locations,
+        locations=np.insert(revenue_locations, 0, -1),
         variance_type="moment"
     )
 

docs/source/tutorials/oregon.rst

@@ -218,13 +218,13 @@ Cost Analysis with Local PTE
     lpte_simple, lpte_lower_simple, lpte_upper_simple = simple_local_estimator.predict_lpte(
         target_treatment_arm=1,  # Z=1 Selected for treatment (Enrolled)
         control_treatment_arm=0,  # Z=0 Not selected for treatment (Not enrolled)
-        locations=[-1] + outcome_ed_costs_locations
+        locations=np.insert(outcome_ed_costs_locations, 0, -1)
     )
 
     lpte_ml, lpte_lower_ml, lpte_upper_ml = ml_local_estimator.predict_lpte(
         target_treatment_arm=1,  # Z=1 Selected for treatment (Enrolled)
         control_treatment_arm=0,  # Z=0 Not selected for treatment (Not enrolled)
-        locations=[-1] + outcome_ed_costs_locations
+        locations=np.insert(outcome_ed_costs_locations, 0, -1)
     )
 
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
@@ -351,13 +351,13 @@ Visits Analysis with Local PTE
     lpte_simple, lpte_lower_simple, lpte_upper_simple = simple_local_estimator.predict_lpte(
         target_treatment_arm=1,  # Z=1 Selected for treatment (Enrolled)
         control_treatment_arm=0,  # Z=0 Not selected for treatment (Not enrolled)
-        locations=[-1] + outcome_ed_visits_locations
+        locations=np.insert(outcome_ed_visits_locations, 0, -1)
     )
 
     lpte_ml, lpte_lower_ml, lpte_upper_ml = ml_local_estimator.predict_lpte(
         target_treatment_arm=1,  # Z=1 Selected for treatment (Enrolled)
         control_treatment_arm=0,  # Z=0 Not selected for treatment (Not enrolled)
-        locations=[-1] + outcome_ed_visits_locations
+        locations=np.insert(outcome_ed_visits_locations, 0, -1)
     )
 
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))