Experimentation in a Ridesharing Marketplace

eng.lyft.com

If you've done any online experimentation, you may not have had to think too hard about the mechanism by which users are partitioned into treatment and control groups. Most A/B tests use naive randomization, which is completely appropriate for many scenarios. This post explains how experimentation in networks like Lyft's inherently causes interference between the groups, which can significantly impact the validity of the experiment. Highly recommended.

Read more...
Linkedin Revue

Want to receive more content like this in your inbox?