Causal Forecasting at Lyft (Part 1)

Causal Forecasting at Lyft (Part 1)

摘要

Efficiently managing our marketplace is a core objective of Lyft Data Science. That means providing meaningful financial incentives to drivers in order to supply affordable rides while keeping ETAs low under changing market conditions — no easy task!

Lyft’s tool chest contains a variety of market management products: rider coupons, driver bonuses, and pricing, to name a few. Using these efficiently requires a strong understanding of their downstream consequences — everything from counts of riders opening the Lyft app (“sessions”) to financial metrics.

To complicate the science further, our data is heavily confounded by our previous decisions, so a merely correlational model would fail us. Sifting out causal relationships is the only option for making smart forward looking decisions.