Selection Bias Correction in Retail Intelligence
Quick summary
arXiv:2608.26156v1 Announce Type: new Abstract: Retail intelligence often relies on monitoring popular, high-velocity products, potentially biasing economic indicators by ignoring the "long tail" of niche items. This simulation study investigates selection bias in inflation estimation and compares correction methods across diverse data-generating processes. Through 400 Monte Carlo replications spanning four scenarios--aligned step functions, smooth gradients, misaligned breaks, and polynomial relationships--we test the robustness of Inverse Probability Weighting (IPW) with five specifications
Key takeaways
- arXiv:2608.26156v1 Announce Type: new Abstract: Retail intelligence often relies on monitoring popular, high-velocity products, potentially biasing economic indicators by ignoring the "long tail" of niche items.
- This simulation study investigates selection bias in inflation estimation and compares correction methods across diverse data-generating processes.
- Through 400 Monte Carlo replications spanning four scenarios--aligned step functions, smooth gradients, misaligned breaks, and polynomial relationships--we test the robustness of Inverse Probability Weighting (IPW) with five specifications
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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