Statisticians like me say CORRELATION ISN'T CAUSATION but that's not the whole story.
There are at least FOUR different scenarios!
A thread. 🧵
1. CORRELATED BY CHANCE. There's always a possibility that variables will correlate by chance. If you have a lot of data, you're almost certain to get a few high correlations. You will know you're in this situation if the same variables are much less correlated in new data.
2. CORRELATED DUE TO STRUCTURE. Clocks are correlated with each other but there's nothing about Clock A that can be changed in order to cause a change in Clock B or vice versa. There is no third thing you can change that will cause both clocks to change. There is no causation.
You might be tempted to say that the clocks have the common cause of being created by humans. Imagine two random stars that have a cyclical change in brightness every 24 hours. They will be correlated as well. It's not about who created them. It's about their similar structure.
3. MURKY CAUSATION. In the simplest case, if A and B are correlated and there is some causation then this could mean that A causes B, B causes A or some third thing C causes both A and B. In the most complex case, there could be complicated feedback loops between A and B.
In these cases, when we say "correlation isn't causation", what we mean is that we can't identify exactly what kind of causation there is but there is some.
4. EVEN MURKIER CAUSATION. A and B might not be related at all in the real world but something about your data collection may have caused data about A to be related to data about B. Technically, you could say you or your data collection are the cause of the correlation.
However, in the context of the original variables themselves and the real world, A is not causally related to B.
Hope this was educational! 🧵
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You may have heard hallucinations are a big problem in AI, that they make stuff up that sounds very convincing, but isn't real.
Hallucinations aren't the real issue. The real issue is Exact vs Approximate, and it's a much, much bigger problem.
When you fit a curve to data, you have choices.
You can force it to pass through every point, or you can approximate the overall shape of the points without hitting any single point exactly.
When it comes to AI, there's a similar choice.
These models are built to match the shape of language. In any given context, the model can either produce exactly the text it was trained on, or it can produce text that's close but not identical
I’m deeply skeptical of the AI hype because I’ve seen this all before. I’ve watched Silicon Valley chase the dream of easy money from data over and over again, and they always hit a wall.
Story time.
First it was big data. The claim was that if you just piled up enough data, the answers would be so obvious that even the dumbest algorithm or biggest idiot could see them.
Models were an afterthought. People laughed at you if you said the details mattered.
Unsurprisingly, it didn't work out.
Next came data scientists. The idea was simple: hire smart science PhDs, point them at your pile of data, wait for the monetizable insights to roll in.
As a statistician, this is extremely alarming. I’ve spent years thinking about the ethical principles that guide data analysis. Here are a few that feel most urgent:
RESPECT AUTONOMY
Collect data only with meaningful consent. People deserve control over how their information is used.
Example: If you're studying mobile app behavior, don’t log GPS location unless users explicitly opt in and understand the implications.
DO NO HARM
Anticipate and prevent harm, including breaches of privacy and stigmatization.
Example: If 100% of a small town tests positive for HIV, reporting that stat would violate privacy. Aggregating to the county level protects individuals while keeping the data useful.
Hot take: Students using chatgpt to cheat are just following the system’s logic to its natural conclusion, a system that treats learning as a series of hoops to jump through, not a path to becoming more fully oneself.
The tragedy is that teachers and students actually want the same thing, for the student to grow in capability and agency, but school pits them against each other, turning learning into compliance and grading into surveillance.
Properly understood, passing up a real chance to learn is like skipping out on great sex or premium ice cream. One could but why would one want to?
If you think about how statistics works it’s extremely obvious why a model built on purely statistical patterns would “hallucinate”. Explanation in next tweet.
Very simply, statistics is about taking two points you know exist and drawing a line between them, basically completing patterns.
Sometimes that middle point is something that exists in the physical world, sometimes it’s something that could potentially exist, but doesn’t.
Imagine an algorithm that could predict what a couple’s kids might look like. How’s the algorithm supposed to know if one of those kids it predicted actually exists or not?
The child’s existence has no logical relationship to the genomics data the algorithm has available.