apply men/women to male/female

Author: yasui-salmon

docs/source/tutorials/hillstrom.rst

@@ -350,12 +350,12 @@ Defining Subgroups
 .. code-block:: python
 
     # Define subgroup masks based on purchase history
-    male_purchasers = (df['mens'] == 1)
-    female_purchasers = (df['womens'] == 1)
+    mens_purchasers = (df['mens'] == 1)
+    womens_purchasers = (df['womens'] == 1)
 
-    print(f"Male purchaser segment: {male_purchasers.sum():,} customers")
-    print(f"Female purchaser segment: {female_purchasers.sum():,} customers")
-    print(f"Overlap: {(male_purchasers & female_purchasers).sum():,} customers")
+    print(f"Men's merchandise purchaser segment: {mens_purchasers.sum():,} customers")
+    print(f"Women's merchandise purchaser segment: {womens_purchasers.sum():,} customers")
+    print(f"Overlap: {(mens_purchasers & womens_purchasers).sum():,} customers")
 
 Average Treatment Effects by Subgroup
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -366,40 +366,40 @@ Let's first compute the average treatment effects (ATEs) to quantify the overall
 
     # Compute ATEs for each campaign-subgroup combination
     # Women's Email Campaign
-    ate_women_male = (revenue[(D==2) & male_purchasers].mean() -
-                      revenue[(D==0) & male_purchasers].mean())
-    ate_women_female = (revenue[(D==2) & female_purchasers].mean() -
-                        revenue[(D==0) & female_purchasers].mean())
+    ate_women_male = (revenue[(D==2) & mens_purchasers].mean() -
+                      revenue[(D==0) & mens_purchasers].mean())
+    ate_women_female = (revenue[(D==2) & womens_purchasers].mean() -
+                        revenue[(D==0) & womens_purchasers].mean())
 
     # Men's Email Campaign
-    ate_men_male = (revenue[(D==1) & male_purchasers].mean() -
-                    revenue[(D==0) & male_purchasers].mean())
-    ate_men_female = (revenue[(D==1) & female_purchasers].mean() -
-                      revenue[(D==0) & female_purchasers].mean())
+    ate_men_male = (revenue[(D==1) & mens_purchasers].mean() -
+                    revenue[(D==0) & mens_purchasers].mean())
+    ate_men_female = (revenue[(D==1) & womens_purchasers].mean() -
+                      revenue[(D==0) & womens_purchasers].mean())
 
     print("Average Treatment Effects by Subgroup:")
     print("\nWomen's Email Campaign:")
-    print(f"  Male Purchasers:   ATE = ${ate_women_male:.4f}")
-    print(f"  Female Purchasers: ATE = ${ate_women_female:.4f}")
+    print(f"  Men's Merch. Purchasers:   ATE = ${ate_women_male:.4f}")
+    print(f"  Women's Merch. Purchasers: ATE = ${ate_women_female:.4f}")
     print("\nMen's Email Campaign:")
-    print(f"  Male Purchasers:   ATE = ${ate_men_male:.4f}")
-    print(f"  Female Purchasers: ATE = ${ate_men_female:.4f}")
+    print(f"  Men's Merch. Purchasers:   ATE = ${ate_men_male:.4f}")
+    print(f"  Women's Merch. Purchasers: ATE = ${ate_men_female:.4f}")
 
 Expected output::
 
     Average Treatment Effects by Subgroup:
 
     Women's Email Campaign:
-      Male Purchasers:   ATE = $0.2564
-      Female Purchasers: ATE = $0.5442
+      Men's Merch. Purchasers:   ATE = $0.2564
+      Women's Merch. Purchasers: ATE = $0.5442
 
     Men's Email Campaign:
-      Male Purchasers:   ATE = $0.8966
-      Female Purchasers: ATE = $0.8412
+      Men's Merch. Purchasers:   ATE = $0.8966
+      Women's Merch. Purchasers: ATE = $0.8412
 
 These results reveal important patterns:
 
-- **Women's Email Campaign**: Shows 2× stronger effect for female purchasers ($0.54) vs male purchasers ($0.26)
+- **Women's Email Campaign**: Shows 2× stronger effect for women's merchandise purchasers ($0.54) vs men's merchandise purchasers ($0.26)
 - **Men's Email Campaign**: Demonstrates consistent strong effects across both segments ($0.84-$0.89)
 
 While these averages provide a useful summary, they don't tell us *how* customer spending distributions change. The distributional and probability treatment effect analyses that follow reveal the complete picture of campaign effectiveness.
@@ -411,13 +411,13 @@ Beyond the average effects, let's examine how the Women's Email campaign shifts
 
 .. code-block:: python
 
-    # Analyze male purchaser segment
+    # Analyze men's merchandise purchaser segment
     estimator_male = dte_adj.SimpleDistributionEstimator()
-    estimator_male.fit(X[male_purchasers], D[male_purchasers], revenue[male_purchasers])
+    estimator_male.fit(X[mens_purchasers], D[mens_purchasers], revenue[mens_purchasers])
 
-    # Analyze female purchaser segment
+    # Analyze women's merchandise purchaser segment
     estimator_female = dte_adj.SimpleDistributionEstimator()
-    estimator_female.fit(X[female_purchasers], D[female_purchasers], revenue[female_purchasers])
+    estimator_female.fit(X[womens_purchasers], D[womens_purchasers], revenue[womens_purchasers])
 
     # Define evaluation points
     locations = np.linspace(0, 500, 51)
@@ -441,13 +441,13 @@ Beyond the average effects, let's examine how the Women's Email campaign shifts
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
 
     plot(locations, dte_women_male, lower_women_male, upper_women_male,
-         title="Women's Email vs Control\nMale Purchaser Segment",
+         title="Women's Email vs Control\nMen's Merch. Purchasers",
          xlabel="Spending ($)", ylabel="Distribution Treatment Effect",
          color="purple", ax=ax1)
     ax1.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
 
     plot(locations, dte_women_female, lower_women_female, upper_women_female,
-         title="Women's Email vs Control\nFemale Purchaser Segment",
+         title="Women's Email vs Control\nWomen's Merch. Purchasers",
          xlabel="Spending ($)", ylabel="Distribution Treatment Effect",
          color="green", ax=ax2)
     ax2.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
@@ -460,7 +460,7 @@ Beyond the average effects, let's examine how the Women's Email campaign shifts
    :width: 800px
    :align: center
 
-**Key Finding for Women's Email Campaign**: The distributional treatment effects reveal that women's email campaigns are significantly more effective for the female purchaser segment (right panel) compared to the male purchaser segment (left panel). The DTE curves show that women's emails reduce the probability of low spending levels (negative DTE at lower thresholds) for female purchasers, indicating a shift toward higher spending. In contrast, the male purchaser segment shows minimal or non-significant effects across most of the spending distribution, with confidence intervals overlapping zero.
+**Key Finding for Women's Email Campaign**: The distributional treatment effects reveal that women's email campaigns are significantly more effective for the women's merchandise purchaser segment (right panel) compared to the men's merchandise purchaser segment (left panel). The DTE curves show that women's emails reduce the probability of low spending levels (negative DTE at lower thresholds) for women's merchandise purchasers, indicating a shift toward higher spending. In contrast, the men's merchandise purchaser segment shows minimal or non-significant effects across most of the spending distribution, with confidence intervals overlapping zero.
 
 Distribution Treatment Effects: Men's Email Campaign
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -488,13 +488,13 @@ Now let's examine how the Men's Email campaign affects spending distributions:
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
 
     plot(locations, dte_men_male, lower_men_male, upper_men_male,
-         title="Men's Email vs Control\nMale Purchaser Segment",
+         title="Men's Email vs Control\nMen's Merch. Purchasers",
          xlabel="Spending ($)", ylabel="Distribution Treatment Effect",
          color="purple", ax=ax1)
     ax1.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
 
     plot(locations, dte_men_female, lower_men_female, upper_men_female,
-         title="Men's Email vs Control\nFemale Purchaser Segment",
+         title="Men's Email vs Control\nWomen's Merch. Purchasers",
          xlabel="Spending ($)", ylabel="Distribution Treatment Effect",
          color="green", ax=ax2)
     ax2.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
@@ -507,7 +507,7 @@ Now let's examine how the Men's Email campaign affects spending distributions:
    :width: 800px
    :align: center
 
-**Key Finding for Men's Email Campaign**: In contrast to women's email campaigns, men's email campaigns show consistent effectiveness across both purchase history segments. The DTE curves in both panels show similar patterns, with negative values at lower spending levels indicating reduced probability of low spending for both male and female purchasers. This suggests that men's emails have broad appeal regardless of whether customers historically purchased men's or women's merchandise.
+**Key Finding for Men's Email Campaign**: In contrast to women's email campaigns, men's email campaigns show consistent effectiveness across both purchase history segments. The DTE curves in both panels show similar patterns, with negative values at lower spending levels indicating reduced probability of low spending for both male and women's merchandise purchasers. This suggests that men's emails have broad appeal regardless of whether customers historically purchased men's or women's merchandise.
 
 Probability Treatment Effects: Women's Email Campaign
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -538,14 +538,14 @@ While DTE shows how cumulative distributions shift, Probability Treatment Effect
 
     plot(locations, pte_women_male, pte_lower_women_male, pte_upper_women_male,
          chart_type="bar",
-         title="Women's Email vs Control\nMale Purchaser Segment",
+         title="Women's Email vs Control\nMen's Merch. Purchasers",
          xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
          color="purple", ax=ax1)
     ax1.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
 
     plot(locations, pte_women_female, pte_lower_women_female, pte_upper_women_female,
          chart_type="bar",
-         title="Women's Email vs Control\nFemale Purchaser Segment",
+         title="Women's Email vs Control\nWomen's Merch. Purchasers",
          xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
          color="green", ax=ax2)
     ax2.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
@@ -558,7 +558,7 @@ While DTE shows how cumulative distributions shift, Probability Treatment Effect
    :width: 800px
    :align: center
 
-**Interval-Specific Insights**: The PTE bar charts reveal the mechanism behind the average treatment effect. For female purchasers (right panel), women's emails significantly reduce the probability of zero spending (non-purchasers converting to purchasers), which is the primary driver of the positive ATE. However, no significant increase in high spending categories is observed. For male purchasers (left panel), the effects are much smaller and less consistent, confirming the limited impact suggested by the ATE and DTE analyses.
+**Interval-Specific Insights**: The PTE bar charts reveal the mechanism behind the average treatment effect. For women's merchandise purchasers (right panel), women's emails significantly reduce the probability of zero spending (non-purchasers converting to purchasers), which is the primary driver of the positive ATE. However, no significant increase in high spending categories is observed. For men's merchandise purchasers (left panel), the effects are much smaller and less consistent, confirming the limited impact suggested by the ATE and DTE analyses.
 
 Probability Treatment Effects: Men's Email Campaign
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -587,14 +587,14 @@ Let's examine which spending categories are most affected by men's email campaig
 
     plot(locations, pte_men_male, pte_lower_men_male, pte_upper_men_male,
          chart_type="bar",
-         title="Men's Email vs Control\nMale Purchaser Segment",
+         title="Men's Email vs Control\nMen's Merch. Purchasers",
          xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
          color="purple", ax=ax1)
     ax1.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
 
     plot(locations, pte_men_female, pte_lower_men_female, pte_upper_men_female,
          chart_type="bar",
-         title="Men's Email vs Control\nFemale Purchaser Segment",
+         title="Men's Email vs Control\nWomen's Merch. Purchasers",
          xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
          color="green", ax=ax2)
     ax2.axhline(y=0, color='black', linestyle='--', linewidth=0.8, alpha=0.5)
@@ -607,7 +607,7 @@ Let's examine which spending categories are most affected by men's email campaig
    :width: 800px
    :align: center
 
-**Interval-Specific Insights**: Men's email campaigns show similar PTE patterns across both segments (left and right panels). The key mechanism is twofold: (1) significant reduction in zero spending probability (converting non-purchasers to purchasers), and (2) increased probability in the $40-100 spending range. This dual effect—both purchase conversion and mid-range spending increases—occurs consistently across both male and female purchaser segments, confirming the broad effectiveness of men's campaigns.
+**Interval-Specific Insights**: Men's email campaigns show similar PTE patterns across both segments (left and right panels). The key mechanism is twofold: (1) significant reduction in zero spending probability (converting non-purchasers to purchasers), and (2) increased probability in the $40-100 spending range. This dual effect—both purchase conversion and mid-range spending increases—occurs consistently across both male and women's merchandise purchaser segments, confirming the broad effectiveness of men's campaigns.
 
 Key Insights from Subgroup Analysis
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -616,29 +616,29 @@ Combining Average Treatment Effects (ATE), Distribution Treatment Effects (DTE),
 
 **1. Campaign Targeting Effectiveness (from ATE)**
 
-- Women's email campaigns show 2× stronger average effects for female purchasers ($0.54) vs male purchasers ($0.26)
+- Women's email campaigns show 2× stronger average effects for women's merchandise purchasers ($0.54) vs men's merchandise purchasers ($0.26)
 - Men's email campaigns demonstrate consistent strong effects across both segments ($0.89-$0.84)
 - This suggests women's campaigns benefit from precise targeting, while men's campaigns have broader appeal
 
 **2. Distributional Shifts Beyond Averages (from DTE)**
 
-- For women's emails, the female purchaser segment shows negative DTE at lower spending thresholds, indicating a systematic shift away from low-spending behavior
-- Male purchasers show minimal distributional changes from women's emails, with confidence intervals overlapping zero at most thresholds
+- For women's emails, the women's merchandise purchaser segment shows negative DTE at lower spending thresholds, indicating a systematic shift away from low-spending behavior
+- Men's merchandise purchasers show minimal distributional changes from women's emails, with confidence intervals overlapping zero at most thresholds
 - Men's emails produce similar distributional patterns across both segments, confirming broad effectiveness
 
 **3. Spending Category Changes (from PTE)**
 
 - PTE analysis reveals *which specific spending intervals* change in response to campaigns, particularly identifying the mechanisms behind average effects
-- **Women's emails**: For female purchasers, the primary effect is converting non-purchasers to purchasers (significant reduction in zero spending probability). No significant increase in high spending categories was observed.
+- **Women's emails**: For women's merchandise purchasers, the primary effect is converting non-purchasers to purchasers (significant reduction in zero spending probability). No significant increase in high spending categories was observed.
 - **Men's emails**: Show a dual mechanism across both segments: (1) converting non-purchasers to purchasers (zero spending reduction), and (2) increasing purchases in the $40-100 range
 - PTE enables identification of behavioral change mechanisms that are invisible in average treatment effects alone—specifically revealing that lift comes primarily from purchase conversion (0→1 effect) rather than spending increases among existing purchasers
 
 **4. Strategic Implications**
 
 Based on these findings, several practical implications emerge:
 
-- **For Women's Campaigns**: Target customers with history of purchasing women's merchandise to maximize ROI. The PTE analysis reveals that effectiveness comes primarily from converting non-purchasers to purchasers among female purchaser segments, rather than increasing spending among existing buyers.
-- **For Men's Campaigns**: Deploy broadly as they produce consistent positive effects across diverse customer segments. Both male and female purchasers show both purchase conversion and mid-range spending increases, suggesting broader appeal.
+- **For Women's Campaigns**: Target customers with history of purchasing women's merchandise to maximize ROI. The PTE analysis reveals that effectiveness comes primarily from converting non-purchasers to purchasers among women's merchandise purchaser segments, rather than increasing spending among existing buyers.
+- **For Men's Campaigns**: Deploy broadly as they produce consistent positive effects across diverse customer segments. Both male and women's merchandise purchasers show both purchase conversion and mid-range spending increases, suggesting broader appeal.
 - **Resource Allocation**: One practical implication is to prioritize precise targeting for gender-specific content (women's emails) but invest confidently in broad deployment for broadly appealing content (men's emails).
 
 **5. Methodological Value**