/Tech12h ago

Judea Pearl Questions Next Steps For Causal Models On Human Decisions

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Judea Pearl@yudapearl#297inTech

I am honored to be part of this philosophical history of "Why", but I do not clearly understand the part that is still missing. What kind of answers would be satisfactory and what kind of knowledge would be required to answer them? @BruceTedesco

Bruce Grey Tedesco@BruceTedesco

From antiquity to 2000. That’s roughly how long it took to turn “why” into something a machine could compute. Aristotle started it. To know a thing, he argued, is to grasp its why — its causes, not just its surface. Knowledge was explanation, never description. Hume broke it. Causation, he said, is never actually observed — we see one thing follow another and call it cause out of habit. What felt like bedrock became inference. Lewis rebuilt it with counterfactuals: C caused E if, without C, E would not have happened. And in 2000, Judea Pearl gave the whole question its mathematics — causal diagrams, interventions, a formal grammar for “what if.” Twenty-three centuries of philosophy, finally executable. I have that first edition on my desk. What strikes me is what’s still unfinished. Most of today’s energy goes to the machine’s why — explaining why a model produced an output. Important work. But there’s an older why that stays stubborn: why a person chose this over that. Why a customer stayed, or left, or never came at all. Pearl built the engine. Pointing it at human decisions, rather than algorithmic ones, is the part still open. Aristotle would have found that question familiar. @yudapearl

2:27 PM · Jun 10, 2026 · 2.4K Views
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Users praise Judea Pearl's causal models for fully answering why questions about human decisions and forcing formal causality in AI after 2000 years.

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Judea Pearl@yudapearl

It's a good place to share my theory on why it took 2000 years: It was only due the advent of AI that scientists were forced to explain what cause and effect are to brainless robots, and this in turns, forced them to stop philosophizing and start explicating things formally and algorithmically.

Bruce Grey Tedesco@BruceTedesco

From antiquity to 2000. That’s roughly how long it took to turn “why” into something a machine could compute. Aristotle started it. To know a thing, he argued, is to grasp its why — its causes, not just its surface. Knowledge was explanation, never description. Hume broke it. Causation, he said, is never actually observed — we see one thing follow another and call it cause out of habit. What felt like bedrock became inference. Lewis rebuilt it with counterfactuals: C caused E if, without C, E would not have happened. And in 2000, Judea Pearl gave the whole question its mathematics — causal diagrams, interventions, a formal grammar for “what if.” Twenty-three centuries of philosophy, finally executable. I have that first edition on my desk. What strikes me is what’s still unfinished. Most of today’s energy goes to the machine’s why — explaining why a model produced an output. Important work. But there’s an older why that stays stubborn: why a person chose this over that. Why a customer stayed, or left, or never came at all. Pearl built the engine. Pointing it at human decisions, rather than algorithmic ones, is the part still open. Aristotle would have found that question familiar. @yudapearl

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Bruce Grey Tedesco@BruceTedesco

@yudapearl From my view, Dr. Pearl nothing is missing. You have answered all the WHY questions in the past and satisfied all in 2000. My writing is not clear. You have brought clear and useful science. I use causal inference models in my work. Thank for responding. Best, -Bruce

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David@Foreman1David

@yudapearl @BruceTedesco Active Inference & other Bayesian Brain theories call the answer prediction weighting, presumably set by benefit delivered through evolution or outcome processing / updating. The Book of Why (& Markov blankets) helps us understand our sensory systems actively making our world.

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