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docs(tutorials): fix broken Hillstrom tutorial code #137

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Merged
TomeHirata merged 1 commit into from
Aug 30, 2026
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docs(tutorials): fix broken Hillstrom tutorial code #137

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14 changes: 7 additions & 7 deletions docs/docs/tutorials/hillstrom.md
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@ print(f"Dataset shape: {df.shape}")
print(f"Average spend by segment:\n{df.groupby('segment')['spend'].mean()}")

# Prepare the data for dte_adj analysis
# Create treatment indicator: 0=No E-Mail, 1=Mens E-Mail, 2=Women E-Mail
treatment_mapping = {'No E-Mail': 0, 'Mens E-Mail': 1, 'Women E-Mail': 2}
# Create treatment indicator: 0=No E-Mail, 1=Mens E-Mail, 2=Womens E-Mail
treatment_mapping = {'No E-Mail': 0, 'Mens E-Mail': 1, 'Womens E-Mail': 2}
D = df['segment'].map(treatment_mapping).values

# Use spend as the outcome variable (revenue)
Expand Down Expand Up @@ -62,7 +62,7 @@ print(f"Women's Email: ${revenue[D==2].mean():.2f}")
print("\nConversion Rates:")
print(f"No Email: {df[df['segment']=='No E-Mail']['conversion'].mean():.3f}")
print(f"Men's Email: {df[df['segment']=='Mens E-Mail']['conversion'].mean():.3f}")
print(f"Women's Email: {df[df['segment']=='Women E-Mail']['conversion'].mean():.3f}")
print(f"Women's Email: {df[df['segment']=='Womens E-Mail']['conversion'].mean():.3f}")
```

### Email Campaign Effectiveness Analysis
Expand Down Expand Up @@ -151,14 +151,14 @@ import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

# Women's vs Control PTE
plot(revenue_locations[1:], pte_women_ctrl, pte_lower_women_ctrl, pte_upper_women_ctrl,
plot(revenue_locations, pte_women_ctrl, pte_lower_women_ctrl, pte_upper_women_ctrl,
chart_type="bar",
title="Women's Email vs Control",
xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
ax=ax1)

# Men's vs Control PTE
plot(revenue_locations[1:], pte_men_ctrl, pte_lower_men_ctrl, pte_upper_men_ctrl,
plot(revenue_locations, pte_men_ctrl, pte_lower_men_ctrl, pte_upper_men_ctrl,
chart_type="bar",
title="Men's Email vs Control",
xlabel="Spending Category ($)", ylabel="Probability Treatment Effect",
Expand Down Expand Up @@ -268,14 +268,14 @@ pte_ml, pte_lower_ml, pte_upper_ml = ml_estimator.predict_pte(
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

# Simple estimator
plot(revenue_locations[1:], pte_simple, pte_lower_simple, pte_upper_simple,
plot(revenue_locations, pte_simple, pte_lower_simple, pte_upper_simple,
chart_type="bar",
title="Spending Category Effects: Women's vs Men's (Simple Estimator)",
xlabel="Spending Category", ylabel="Probability Treatment Effect", color="purple",
ax=ax1)

# ML-adjusted estimator
plot(revenue_locations[1:], pte_ml, pte_lower_ml, pte_upper_ml,
plot(revenue_locations, pte_ml, pte_lower_ml, pte_upper_ml,
chart_type="bar",
title="Spending Category Effects: Women's vs Men's (ML-Adjusted Estimator)",
xlabel="Spending Category", ylabel="Probability Treatment Effect",
Expand Down