Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
Copilot wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

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

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6ca06e3
Initial plan
Copilot Feb 18, 2026
d465841
Replace list+ndarray with np.insert for clearer numpy operations in t…
Copilot Feb 18, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions docs/source/tutorials/hillstrom.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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"
)

Expand Down Expand Up @@ -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"
)

Expand Down
8 changes: 4 additions & 4 deletions docs/source/tutorials/oregon.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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))
Expand Down Expand Up @@ -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))
Expand Down