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.