High-Cardinality Marketplace Modeling

Instacart is enhancing its experimentation capabilities by developing novel methods for variance reduction. This includes building predictions at a finer grain (order-level) and aggregating them up, significantly improving statistical power and reducing experimentation time. This approach leverages order-specific covariates to create more accurate pre-treatment predictions, overcoming limitations of traditional region-day CUPED. The system also incorporates a placebo test to ensure feature containment. This post details the application of PyFixest to efficiently handle high-cardinality categorical variables in marketplace modeling, enabling more precise analysis of marketplace algorithms and their impact.