TLDR Tech / Data
Variance Reduction Below the Randomization Grain
Instacart explores advanced variance reduction techniques for A/B testing and causal inference that operate below the randomization grain (at the individual user or session level rather than aggregate), leveraging methods such as CUPED, stratification, and other statistical techniques to significantly reduce experiment variance, enabling more precise measurements and the ability to detect smaller effects.
Read the full story