Causal inference (or more precisely learning causal structure) is exactly the sort of thing I have in mind here. There’s actually a few places in the post where I should distinguish between variables which control an outcome in an information sense (i.e. sufficient to perfectly predict the outcome) vs in a causal sense (i.e. sufficient to cause the outcome under interventions). The main reason I didn’t talk about it directly is because I would have had to explain that distinction, and decided that would be too much of a distraction from the main point.
I think the takeaway of Jason’s talk, as it relates to this post, is that a large chunk of the “science” of achieving consistent outcomes happens in inventors’ workshops rather than scientists’ labs. The problem is still largely similar, regardless of the label applied, but scientists aren’t the only ones doing science.
Causal inference (or more precisely learning causal structure) is exactly the sort of thing I have in mind here. There’s actually a few places in the post where I should distinguish between variables which control an outcome in an information sense (i.e. sufficient to perfectly predict the outcome) vs in a causal sense (i.e. sufficient to cause the outcome under interventions). The main reason I didn’t talk about it directly is because I would have had to explain that distinction, and decided that would be too much of a distraction from the main point.
I think the takeaway of Jason’s talk, as it relates to this post, is that a large chunk of the “science” of achieving consistent outcomes happens in inventors’ workshops rather than scientists’ labs. The problem is still largely similar, regardless of the label applied, but scientists aren’t the only ones doing science.