The Importance of Causal Inference

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directed acyclic graphs or DAGS are a way to constrain models. They show up in graph theory and causal inference it seems, they're all over the place. So at the end of the day, you need a single function which maps from your decisions to your outcomes. And that is in the space of all possible ways things could be a very specific thing. It's good for really collapsing the space of models that you're seeing to estimate.

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.