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Judea Pearl is a professor at UCLA and a winner of the Turing Award, that's generally recognized as the Nobel Prize of computing. He is one of the seminal figures in the field of artificial intelligence, computer science, and statistics. He has developed and championed probabilistic approaches to AI, including Bayesian Networks and profound ideas in causality in general. These ideas are important not just for AI, but to our understanding and practice of science. But in the field of AI, the idea of causality, cause and effect, to many, lies at the core of what is currently missing and what must be developed in order to build truly intelligent systems. For this reason, and many others, his work is worth returning to often.

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What is it, so probability of something happening is something, but then there's a bunch of things happening.

And sometimes they happen together, sometimes not.

So how do you think about correlation of things?

Correlation occurs when 2 things vary together over a very long time, is 1 way of measuring it.

Or when you have a bunch of variables that they all vary cohesively.

Then we call it, we have a correlation here.

And usually, when we think about correlation, we really think causally.

Things cannot be correlated unless there is a reason for them to vary together.

If they don't see each other, why should they vary together?

Yes, hidden in our intuition there is a notion of causation because we cannot grasp any other logic except causation.

And how does conditional probability differ from causation?

Conditional probability, how things vary, when 1 of them stays the same.

Now staying the same means that I have chosen to look only at those incidents where the guy has the same value as the previous 1.

So things that are not correlated before could become correlated.

Like for instance, if I have 2 coins which are uncorrelated, and I choose only those flippings experiments in which a bell rings, and a bell rings when at least 1 of them is a tail, then suddenly I see correlation between the 2 coins.

Because I only look at the cases where the bell rang.

You see, it's my design, with my ignorance essentially, with my audacity to ignore certain incidents, I suddenly create a correlation where it doesn't exist physically.

Right, so that's, you just outlined 1 of the flaws of observing the world and trying to infer something from the math about the world from looking at the correlation.

But the flaws comes if we try to impose causal logic on correlation, it doesn't work too well.

That's what, that has been the majority of science.

Statisticians know that if you condition on a third variable, then you can destroy or create correlations among 2 other variables.

There's nothing surprising, that's why they all dismiss the Simpson Paradox.

Well, there's disciplines like psychology where all the variables are hard to account for.

And so oftentimes there's a leap between correlation to causation.

Who is trying to get causation from causation?

You're not proving causation, but you're sort of discussing it, implying, sort of hypothesizing with our ability

I'll tell you if they are obsolete, or if they are outdated, or they're about to get outdated.

No, no, I was thinking of applied psychology studying, for example, we work with human behavior in semi-autonomous vehicles, how people behave.

And you have to conduct these studies of people driving cars.

The research question, do people fall asleep when the car is driving itself?

Do they fall asleep or do they tend to fall asleep more frequently than the car not driving

And so you measure, you put people in the car, because it's real world, You can't conduct an experiment where you control everything.

Why can't you turn the automatic module on and off?

I mean, there's aspects to it that's unethical because it's testing on public roads.

So you can only use vehicle, they have to, the people, the drivers themselves have to make that choice themselves.

So you just observe when they drive it autonomously and when they don't.

But maybe they turn it off when they're very tired.

Okay, so that you have now uncontrolled experiment.

And we form the correlation, detected, we have to infer causal relationship.

Whether it was the automatic piece that caused them to fall asleep or, So that is an issue that is about

Actually, I should say it's 2,000 years old because we have this experiment by Daniel, but the Babylonian king that wanted the exile the people from Israel that were taken in exile to Babylon to serve the king, he wanted to serve them king's food, which was meat, and Daniel, as a good Jew, couldn't eat non-kosher food, so he asked them to eat vegetarian food.

But the king's overseer said, I'm sorry, but if the king sees that your performance falls below that of other kids, you know, he's going to kill me.

Let's take 4 of us from Jerusalem, okay, give us vegetarian food.

Let's take the other guys to eat the king's food and in about a week's time, we'll test our performance.

And you know the answer, of course, he did the experiment.

And they were so much better than the others.

And the kings nominated them to super position in his case.

So there was a very simple, it's also the same research questions.

We want to know if vegetarian food assist or obstruct your mental ability?

Even Democritus said, if I could discover 1 cause of things, I would rather discover 1 cause than be a king of Persia.

The task of discovering causes was in the mind of ancient people from many, many years ago.

But the mathematics of doing that was only developed in the 1920s.

Science has not provided us with the mathematics to capture the idea of x causes y and y does not cause x.

Because all the equations of physics are symmetrical, algebraic.


Speaker 0: What is correlation? What is it, so probability of something happening is something, but then there's a bunch of things happening. And sometimes they happen together, sometimes not. They're independent or not. So how do you think about correlation of things?

Speaker 1: Correlation occurs when 2 things vary together over a very long time, is 1 way of measuring it. Or when you have a bunch of variables that they all vary cohesively. Then we call it, we have a correlation here. And usually, when we think about correlation, we really think causally. Things cannot be correlated unless there is a reason for them to vary together.

Why should they vary together? If they don't see each other, why should they vary together?

Speaker 0: So underlying it somewhere is causation.

Speaker 1: Yes, hidden in our intuition there is a notion of causation because we cannot grasp any other logic except causation.

Speaker 0: And how does conditional probability differ from causation? So what is conditional probability?

Speaker 1: Conditional probability, how things vary, when 1 of them stays the same. Now staying the same means that I have chosen to look only at those incidents where the guy has the same value as the previous 1. It's my choice as an experimenter. So things that are not correlated before could become correlated. Like for instance, if I have 2 coins which are uncorrelated, and I choose only those flippings experiments in which a bell rings, and a bell rings when at least 1 of them is a tail, then suddenly I see correlation between the 2 coins.

Because I only look at the cases where the bell rang. You see, it's my design, with my ignorance essentially, with my audacity to ignore certain incidents, I suddenly create a correlation where it doesn't exist physically.

Speaker 0: Right, so that's, you just outlined 1 of the flaws of observing the world and trying to infer something from the math about the world from looking at the correlation.

Speaker 1: I don't look at it as a flaw. The world works like that. But the flaws comes if we try to impose causal logic on correlation, it doesn't work too well.

Speaker 0: I mean, but that's exactly what we do. That's what, that has been the majority of science. Is your-

Speaker 1: The majority of naive science. Statisticians know it. Statisticians know that if you condition on a third variable, then you can destroy or create correlations among 2 other variables. They know it, it's in the data. There's nothing surprising, that's why they all dismiss the Simpson Paradox.

Ah, we know it! They don't know anything about it.

Speaker 0: Well, there's disciplines like psychology where all the variables are hard to account for. And so oftentimes there's a leap between correlation to causation. You're imposing.

Speaker 1: What do you mean, leap? Who is trying to get causation from causation?

Speaker 0: You're not proving causation, but you're sort of discussing it, implying, sort of hypothesizing with our ability

Speaker 1: to prove. Which discipline do you have in mind? I'll tell you if they are obsolete, or if they are outdated, or they're about to get outdated. Tell me which 1 do you have in mind.

Speaker 0: Oh, psychology, you know.

Speaker 1: Psychology, what is it, SEM? Structural equation?

Speaker 0: No, no, I was thinking of applied psychology studying, for example, we work with human behavior in semi-autonomous vehicles, how people behave. And you have to conduct these studies of people driving cars.

Speaker 1: Everything starts with a question. What is the research question?

Speaker 0: What is the research question? The research question, do people fall asleep when the car is driving itself?

Speaker 1: Do they fall asleep or do they tend to fall asleep more frequently than the car not driving

Speaker 0: itself? Not driving itself.

Speaker 1: That's a good question, okay.

Speaker 0: And so you measure, you put people in the car, because it's real world, You can't conduct an experiment where you control everything.

Speaker 1: Why can't you turn the automatic module on and off? Because it's on

Speaker 0: road public. I mean, there's aspects to it that's unethical because it's testing on public roads. So you can only use vehicle, they have to, the people, the drivers themselves have to make that choice themselves. And so they regulate that. So you just observe when they drive it autonomously and when they don't.

And then.

Speaker 1: But maybe they turn it off when they're very tired. Yeah, that kind of thing.

Speaker 0: But you don't know those variables.

Speaker 1: Okay, so that you have now uncontrolled experiment.

Speaker 0: Uncontrolled experiment.

Speaker 1: We call it observational study. And we form the correlation, detected, we have to infer causal relationship. Whether it was the automatic piece that caused them to fall asleep or, So that is an issue that is about

Speaker 0: 120

Speaker 1: years old. I should only go

Speaker 0: 100

Speaker 1: years old, okay? Oh, maybe it's not. Actually, I should say it's 2,000 years old because we have this experiment by Daniel, but the Babylonian king that wanted the exile the people from Israel that were taken in exile to Babylon to serve the king, he wanted to serve them king's food, which was meat, and Daniel, as a good Jew, couldn't eat non-kosher food, so he asked them to eat vegetarian food. But the king's overseer said, I'm sorry, but if the king sees that your performance falls below that of other kids, you know, he's going to kill me. Daniel said, let's make an experiment.

Let's take 4 of us from Jerusalem, okay, give us vegetarian food. Let's take the other guys to eat the king's food and in about a week's time, we'll test our performance. And you know the answer, of course, he did the experiment. And they were so much better than the others. And the kings nominated them to super position in his case.

So it was the first experiment, yes. So there was a very simple, it's also the same research questions. We want to know if vegetarian food assist or obstruct your mental ability? And, okay, so the question is very old. Even Democritus said, if I could discover 1 cause of things, I would rather discover 1 cause than be a king of Persia.

The task of discovering causes was in the mind of ancient people from many, many years ago. But the mathematics of doing that was only developed in the 1920s. So science has left us orphaned. Science has not provided us with the mathematics to capture the idea of x causes y and y does not cause x. Because all the equations of physics are symmetrical, algebraic.

The equality sign goes both ways. You

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Judea Pearl: Correlation and Causation | AI Podcast Clips