We’ve traditionally relied on A/B testing at DoorDash to guide our decisions.
Author Archives: Stas Sajin
Sharpening the Blur: Removing dilution to maximize experiment power
When it comes to reducing variance in experiments, the spotlight often falls on sophisticated methods like CUPED (Controlled Experiments Using Pre-Experiment Data).
Addressing the Challenges of Sample Ratio Mismatch in A/B Testing
Experimentation isn’t just a cornerstone for innovation and sound decision-making; it’s often referred to as the gold standard for problem-solving, thanks in part to its roots in the scientific method.
Balancing Velocity and Confidence in Experimentation
Running thousands of experiments effectively means carefully balancing our speed with the necessary controls to maintain trust in experimental outputs–but figuring out that balance is never easy.
Managing Supply and Demand Balance Through Machine Learning
At DoorDash, we want our service to be a daily convenience offering timely deliveries and consistent pricing.