[Frost1x]

>More often than not, when stakeholders require "causality" to make a decision, it takes way too long so they lose patience and end up making a decision without any data at all.

And therein, I believe, lies the problem.

I think the issue is the pressure for science to produce something constantly so in today's world, correlation is causality. Whether or not you believe in deterministic laws that govern reality, correlation is often the easiest approach when looking at a difficult problem and there in lies the rise of much of probabilistic and statistical models in the face of difficulty. Not all cases, but a lot of cases. We don't want to continue trying the hard work of determining definitive casual relations, if they exist and are content with correlative relations.

As someone who grew up fascinated by science because it was science that sought and provided causal relations, I'm often disappointed about the current world of research. I'm not saying this work is easy by any means, it just seems like we often give up anymore after we pick up the low hanging fruit.

[lazyjeff]

I don't think it's necessarily that scientists are avoiding the hard work to show causality. It's that the most interesting causal experiments are often unethical, or the independent variable cannot be hidden like a placebo (so the participants' bias affect the randomization), or it's simply impossible.

I'll use one example from some data I've been looking at, which is whether the covid-19 pandemic has changed how people sleep. To study this using the formal notion of causality requires asking a random 50% of people to sleep as if covid isn't happening. That's obviously both impractical and implausible.

So you can really only look at correlations. But I can show you the correlations, and I bet you will be convinced that the pandemic HAS changed peoples' sleep. Here's some charts if you can take a look: https://jeffhuang.com/covid_sleep/ but there's probably several factors that convince you that this is causal.

First is the pattern of sleep pre-covid is very stable, and feels trustworthy because it goes up and down during weekends, and holidays are visible. So the data is visibly sensitive to changes in the environment. Second, nearly every country reacts similarly when the N is separated, so even if there's some large group of people somewhere that are outliers (say, some policy by California that everyone needs to go to bed later), it would only affect that one country they are in, not each country separately the same way. Finally, the patterns of sleep post-covid are also stable with similar patterns as pre-covid, but just shifted.

I'm not sure if there's formal ways of representing these concepts, but I feel humans understand these intuitively.

[brittanymdavis1]

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.

[brittanymdavis1]

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.