The Cost of Searching Over DAG Space

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Doing like search over DAG space is just prohibitively expensive and too complicated. I would rather start with the, pretend you know nothing. And then any data that you have that might help inform your assumption or your idea is just a gift as opposed to don't ask me to make come up with any ideas. It's a much bigger ask to get a causal model than it is virtually anything else.

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.