Timothy Stanley: There's a degree of self-awareness required to write and use a user manual. He says the exercise of doing it forces you to become forward to be self-aware. Stanley: Direct feedback is incredibly hard, so like it requires a level of thoughtfulness about your ways of working that I think is really beneficial.
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.