Causal Forecasting at Lyft (Part 2)

Causal Forecasting at Lyft (Part 2)

摘要

In our last blog, we discussed how managing our business effectively comes down to, in large part, making causally valid forecasts based on our decisions. Such forecasts accurately predict the future while still agreeing with experiment (e.g. increasing prices by X will decrease conversion by Y). With this, we can optimize our decisions to yield a desirable future.

But there remains a gap between theory and the implementation that makes it a reality. In this blog, we will discuss the design of software and algorithms we use to bridge this gap.