Researchers find correlation assume causation...
We use difference-in-difference models (Eq (1)) to test whether a rise in the prevalence of Airbnb in a census tract in one year predicts increases in crime and disorder in the following year.
...
The models control for tract-level and year fixed effects. In order to make the parameter estimates that follow more interpretable, we note that the average census tract in the average year experienced 11.32 events of private conflict, 7.68 events of public social disorder, and 28.58 events of public violence per 1,000 residents.
I've not dug into the study enough to vouch for its quality as a whole - but it's clear the researchers are plenty aware of the differences between correlation and causation and are at least attempting to address them. This is actually often the case with scientific papers, even if it's lost in the media coverage of them.
"The large-scale conversion of housing units into short-term rentals undermines a neighborhood’s social organization, and in turn its natural ability...to counteract and discourage crime,"
and the research reeks of p-hacking and non-reproducibility:
Spain:
>"It encourages the concentration of tourists who, due to their characteristics, are suitable targets for victimisation," Maldonado-Guzmán said.
> The researchers found that there was a positive correlation between higher penetration of Airbnb properties in an area – for example buildings containing multiple Airbnb lets – and a rise in violence. However, crime types associated with rowdy visitors, like drunkenness and noise complaints, as well as private conflicts, did not increase.
> "It's not the number of Airbnb tourists who stay in a neighborhood that causes an increase in criminal activities," said Professor Babak Heydari from Northeastern University.
But I'm just talking about the statistics here, and specifically that saying "correlation is not causation" is a bit overused. Researchers know about it too, those four words don't magically dismiss all statistical studies. Most modern statistical approaches are explicitly built to try and help address these sorts of concerns.
There could well be other flaws with their statistics, and even if there is causation they could be failing at theoretically motivating or connecting it to their overall narrative. But it takes more than four words to make that case.
EDIT - just acknowledging that you've since edited your comment to add concerns about p-hacking and reproducibility. And that may be the case - but it wasn't what I was responding to in my initial comment.
I think that term has erupted into popularity with the widespread adoption of AI, which is intellectually bankrupt. With AI you can find correlation between things, and draw a very basic rudimentary conclusion, but never actually know why this happens (the causation), in this day and age.
For example, let's apply an unethical use of AI. Let's say an individual goes to the grocery store weekly and buys a dozen eggs and 1 container of dry shampoo (for washing your hair without water), every single week for the past 2 months. With AI and the hoarding of data, it can be found that this individual is going to die in the next 6 months to a 95% confidence interval.
The individual gets harassing ads during this, even though they are not going to die. The ads, of course, in this day and age, play into everyone’s hopes and fears anyways, which is abusive.
‘Causal linkage’ is great in theory. In reality it often shows directionality but not causality. This is a prime example.
More seriously - I'm well aware of the difficulties of causality, and also use causal direction as a great illustration of them. As I've said in pretty much every comment here - I'm not championing the study, I simply haven't done a deep enough pass to have a strong opinion, and it may have any number of subtle flaws (off the cuff my biggest concern is that they're focused on one city, and I'd like to see similar results elsewhere, preferably in different geographic areas and cultures).
In other words, yes - more controls like you said. But I am responding to the overuse of a simple statistical argument in the face of studies that, whatever flaws they have, are not cases of "the researcher forgot the controls." Demographics, income, and homeownership are actually not bad features to have I'd say, and again it seems like most of the large claims bothering people are from the coverage and not the research. To refute research you generally need to dig into the details of the research itself.
This result supports the notion that the prevalence of Airbnb listings erodes the natural ability of a neighborhood to prevent crime, but does not support the interpretation that elevated numbers of tourists bring crime with them.
And again - I'm just responding to the idea that saying "correlation is not causation" can allow one to dismiss any statistical study. The study may have flaws, may overstate its results, could be completely terrible in fact - but the people who did it know about correlation and causation, and refuting them requires going deeper than that. In general, it requires looking at their paper, not the news coverage of it.