I think you really have to know about things that are only in people's brains. But everyone has that intuition that you want the data to just tell you everything, but in certain environments, I just don't think it's possible. That is like the thing I'm dealing with on such a regular basis now: we've built this giant model and it's had, I don't know how many people,. probably 10 people have inserted their assumptions into the system; they're missing history of why each assumption was added. And so you, we have to go through this period of training new people.
What causes us to keep returning to the topic of causal inference on this show? DAG if we know! Whether or not you're familiar with directed acyclic graphs (or… DAGs) in the context of causal inference, this episode is likely for you! DJ Rich, a data scientist at Lyft, joined us to discuss causality — why it matters, why it's tricky, and what happens when you tackle causally modelling the complexity of a large-scale, two-sided market! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.