brittanymdavis1

Data Skeptic podcast is currently doing a series on K-means applications (similar to KNN). Would recommend it for anyone exploring the topic: https://dataskeptic.com/episodes/k-means

You can use causation to make a decision… and you can use correlation to make a decision. Each with different implications of course - but depending on the context both can be used to do the same task.

> Personally, I'm not quite positive that I buy that causation is that hard to establish in many cases.

Do you mean it's not hard from an analytical point of view / from a practical data gathering perspective - or both?

I've found that needing causation leads to big delays in backlogs, especially when it's required for every insight, but I'm curious if you've seen it to be different.

Unfortunately this pressure - especially in research communities - has led to a lot of p-hacking.

https://www.pnas.org/content/117/24/13386

It's similar in a business context - the "pressure" of finding a causal result (especially in situations using AB testing) lead to poor analysis practices in order to find something significant.

At the end of the day, a correlation analysis will never replace a causal study. So really un-intuitive relationships would likely have never been uncovered with this approach. But I believe most of the questions we're typically asking - especially in a business context - are super intuitive and, even with a big causal study, they are never that "surprising" - don't you agree?

Right - The point about human intuition is an important one. It's a key part of the approach that the blog talks about. When we start from intuition, we significantly improve our hypotheses about what could be correlated. And because no decision making is ever done in a vacuum, it's suggesting we lean into this and use it to our advantage instead of knocking on correlations all the time.

Yes right - and we can actually put that information to some use in some practical way. At least that's the goal.

Interesting idea - depends on how you look at performance, right? There’s engineering overhead of setting up a MAB and it likely requires the skill set of a data scientist or ML engineer to interpret - a scarce resource in practice. Making it a less ideal approach for most bootstrapped teams.

From the accuracy point of view - With the MAB approach, you’d still be at risk of ignoring changes over time (or at least being slower to catch changes because of the historical bias) - meaning MAB may not be best suited for understanding the impact over longer periods of time like this approach would.