Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart

This post details the application of fixed effects models for high-cardinality marketplace modeling, addressing the computational bottleneck of traditional OLS regression when dealing with a large number of categories. It explains how the Frisch-Waugh-Lovell theorem and modern packages like PyFixest bypass these limitations by demeaning data and effectively reducing the size of the Gram matrix. The post also includes benchmarking to demonstrate the real-world impact on processing speed, memory efficiency, and estimator precision.