Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart

Benjamin S. Knight

Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages — specifically Fixest and Pyfixest— bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision.

At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart’s Marketplace team is responsible for surfacing customers’ orders to shoppers, aligning Instacart’s delivery windows with shoppers’ projected availabilities as efficiently as possible.