Stratification Beats IPW in Retail Bias Tests
π‘See why IPW can fail catastrophically on retail long-tail dataβand when stratification is safer.
β‘ 30-Second TL;DR
What Changed
Across 400 Monte Carlo replications, stratification achieved median errors below 0.04 percentage points in three of four scenarios.
Why It Matters
The findings suggest that retail analytics pipelines should not assume IPW is universally reliable, especially when product-selection probabilities differ sharply between popular and niche items. For inflation or demand estimates derived from observational retail data, stratification may provide a safer default when positivity violations are severe.
What To Do Next
Benchmark stratification alongside spline-based IPW on your retail or marketplace estimator, and explicitly test for positivity violations across product segments.
Key Points
- β’Across 400 Monte Carlo replications, stratification achieved median errors below 0.04 percentage points in three of four scenarios.
- β’Stratification maintained strong performance despite deliberately misaligned boundaries between strata and population breaks.
- β’An oracle IPW model still produced 6.06 percentage points of error versus 0.008 for stratification in step-function scenarios.
- β’Spline-based IPW won under smooth polynomial relationships, with median error of 0.007 percentage points versus 0.013 for stratification.
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Original source: ArXiv AI β
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