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Cover Quote: April 2019
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When I began my journey into causation, I was following the tracks of an anomaly. With Bayesian networks, we had taught machines to think in shades of gray, and this was an important step toward humanlike thinking. But we still couldn’t teach machines to understand causes and effects. We couldn’t explain to a computer why turning the dial of a barometer won’t cause rain. Nor could we teach it what ti expect when one of the riflemen on a firing squad changes his mind and decides not to shoot. Without the ability to envision alternate realities and contrast them with the currently existing reality, a machine cannot pass the mini-Turing test; it cannot answer the most basic question that makes us human: “Why?” I took this as an anomaly because I did not anticipate such natural and intuitive questions to reside beyond the reach of the most advanced reasoning systems of the time. Only later did I realize that the same anomaly was afflicting more than just the field of artificial intelligence (AI). The very people who should care the most about “Why?” questions — namely, scientists — were laboring under a statistical culture that denied them the right to ask those questions. Of course they asked them anyway, informally, but they had to cast them as associational questions whenever they wanted to subject them to mathematical analysis.
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- Judea Pearl
The Book of Why, 2018
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