New Study Compares Bias Correction Methods for Retail Long-Tail Data

Retail intelligence often focuses on popular, high-volume products, which can introduce bias into economic indicators like inflation by overlooking the vast array of niche, lower-volume items, known as the "long tail." This research explores different methods to correct this selection bias. The study specifically compares Inverse Probability Weighting (IPW) with stratification across various data scenarios. The findings indicate that stratification generally provides superior performance, particularly in situations where the selection probabilities of products vary dramatically, achieving significantly lower error rates. While IPW with spline propensity models showed an advantage in smooth polynomial relationships, its effectiveness was severely limited in step-function scenarios due to violations of the positivity assumption, a fundamental requirement for causal inference weighting methods. This suggests stratification is a more robust engineering choice for retail long-tail distributions with severe positivity violations.