deploy: f54ef49

Author: TomeHirata

_sources/tutorials/hillstrom.rst.txt

@@ -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"
     )
 

_sources/tutorials/oregon.rst.txt

@@ -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))

searchindex.js

@@ -159,15 +159,15 @@ <h2>Spending Category Effects: Each Campaign vs Control<a class="headerlink" hre
 <span class="n">pte_women_ctrl</span><span class="p">,</span> <span class="n">pte_lower_women_ctrl</span><span class="p">,</span> <span class="n">pte_upper_women_ctrl</span> <span class="o">=</span> <span class="n">simple_estimator</span><span class="o">.</span><span class="n">predict_pte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>  <span class="c1"># Women&#39;s email</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># No email control</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">revenue_locations</span><span class="p">,</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">revenue_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
     <span class="n">variance_type</span><span class="o">=</span><span class="s2">&quot;moment&quot;</span>
 <span class="p">)</span>
 
 <span class="c1"># Compute PTE: Men&#39;s email vs Control</span>
 <span class="n">pte_men_ctrl</span><span class="p">,</span> <span class="n">pte_lower_men_ctrl</span><span class="p">,</span> <span class="n">pte_upper_men_ctrl</span> <span class="o">=</span> <span class="n">simple_estimator</span><span class="o">.</span><span class="n">predict_pte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Men&#39;s email</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># No email control</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">revenue_locations</span><span class="p">,</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">revenue_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
     <span class="n">variance_type</span><span class="o">=</span><span class="s2">&quot;moment&quot;</span>
 <span class="p">)</span>
 
@@ -265,14 +265,14 @@ <h2>Revenue Category Analysis with PTE<a class="headerlink" href="#revenue-categ
 <span class="n">pte_simple</span><span class="p">,</span> <span class="n">pte_lower_simple</span><span class="p">,</span> <span class="n">pte_upper_simple</span> <span class="o">=</span> <span class="n">simple_estimator</span><span class="o">.</span><span class="n">predict_pte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Women&#39;s email</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Men&#39;s email</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">revenue_locations</span><span class="p">,</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">revenue_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
     <span class="n">variance_type</span><span class="o">=</span><span class="s2">&quot;moment&quot;</span>
 <span class="p">)</span>
 
 <span class="n">pte_ml</span><span class="p">,</span> <span class="n">pte_lower_ml</span><span class="p">,</span> <span class="n">pte_upper_ml</span> <span class="o">=</span> <span class="n">ml_estimator</span><span class="o">.</span><span class="n">predict_pte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Women&#39;s email</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Men&#39;s email</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">revenue_locations</span><span class="p">,</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">revenue_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
     <span class="n">variance_type</span><span class="o">=</span><span class="s2">&quot;moment&quot;</span>
 <span class="p">)</span>
 

tutorials/hillstrom.html

@@ -224,13 +224,13 @@ <h2>Cost Analysis with Local PTE<a class="headerlink" href="#cost-analysis-with-
 <span class="n">lpte_simple</span><span class="p">,</span> <span class="n">lpte_lower_simple</span><span class="p">,</span> <span class="n">lpte_upper_simple</span> <span class="o">=</span> <span class="n">simple_local_estimator</span><span class="o">.</span><span class="n">predict_lpte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Z=1 Selected for treatment (Enrolled)</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Z=0 Not selected for treatment (Not enrolled)</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">outcome_ed_costs_locations</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">outcome_ed_costs_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 <span class="p">)</span>
 
 <span class="n">lpte_ml</span><span class="p">,</span> <span class="n">lpte_lower_ml</span><span class="p">,</span> <span class="n">lpte_upper_ml</span> <span class="o">=</span> <span class="n">ml_local_estimator</span><span class="o">.</span><span class="n">predict_lpte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Z=1 Selected for treatment (Enrolled)</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Z=0 Not selected for treatment (Not enrolled)</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">outcome_ed_costs_locations</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">outcome_ed_costs_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 <span class="p">)</span>
 
 <span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
@@ -341,13 +341,13 @@ <h2>Visits Analysis with Local PTE<a class="headerlink" href="#visits-analysis-w
 <span class="n">lpte_simple</span><span class="p">,</span> <span class="n">lpte_lower_simple</span><span class="p">,</span> <span class="n">lpte_upper_simple</span> <span class="o">=</span> <span class="n">simple_local_estimator</span><span class="o">.</span><span class="n">predict_lpte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Z=1 Selected for treatment (Enrolled)</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Z=0 Not selected for treatment (Not enrolled)</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">outcome_ed_visits_locations</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">outcome_ed_visits_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 <span class="p">)</span>
 
 <span class="n">lpte_ml</span><span class="p">,</span> <span class="n">lpte_lower_ml</span><span class="p">,</span> <span class="n">lpte_upper_ml</span> <span class="o">=</span> <span class="n">ml_local_estimator</span><span class="o">.</span><span class="n">predict_lpte</span><span class="p">(</span>
     <span class="n">target_treatment_arm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Z=1 Selected for treatment (Enrolled)</span>
     <span class="n">control_treatment_arm</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># Z=0 Not selected for treatment (Not enrolled)</span>
-    <span class="n">locations</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">outcome_ed_visits_locations</span>
+    <span class="n">locations</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">outcome_ed_visits_locations</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 <span class="p">)</span>
 
 <span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>