I am a big fan of what Sean and you are trying to do–I wrote up a chapter about it this weekend, actually. I’m worried that you both have worked for companies where a lot of work has been done to identify relevant dimensions (metrics and categories) and automate causality (or rather: estimating factors on a pre-existing causal graph because that’s the slight of hands the word “causality” does) made sense once you’ve reached that level of maturity.

But to reach that point, before having relevant dimensions, there has to be a lot of work, generally motivated by disappointing experiments. “Why didn’t that work?” is often answered by “Because our goal is too remote from our actions—here’s a better proxy” or “Because this change only makes sense to 8% of our users, here’s how we can split them.”

I’m worried that too many people will think the tool itself is enough and not a complement to the maturity in understanding a company’s user. This ‘solutionism’ is widespread among Data tools: https://www.linkedin.com/posts/bertilhatt_the-potential-gap-...