The Importance of Causal Inference

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DJ: "I think hearing you describe what you're doing at Lyft is just helping me think through like a bunch of stuff that I've done" He says he's more on the e-commerce side in his career. DJ: "We were almost, we almost had a dagg. We called it something else, but we were designing something very similar." The episode ended early for some listeners who didn't want to hear about causal inference and counterfactuals.

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.