There's always that tension because you look at it, we have this PDF that shows what this DAG looks like and it's big. I used to trace through the whole DAG two years ago and I would no longer do that. It's very much like these edges are really your assumptions. So they're all open for criticism. But with enough time, you get that model feedback. You basically say, hey, I changed this thing and this other thing didn't change how I thought it would. That's a problem. We've had enough cycles of that where we've corrected them and then we put something else to kind of patch it up. And that ultimately gives you an overall
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.