The Book of Why: The New Science of Cause and Effect

by Judea Pearl

path analysis “doesn’t lend itself to ‘canned’ programs. The user has to have a hypothesis and must devise an appropriate diagram of multiple causal sequences.” Indeed, Crow put his finger on an essential point: path analysis requires scientific thinking, as does every exercise in causal inference. Statistics, as frequently practiced, discourages it and encourages “canned” procedures instead. Scientists will always prefer routine calculations on data to methods that challenge their scientific knowledge.

Unlike correlation and most of the other tools of mainstream statistics, causal analysis requires the user to make a subjective commitment. She must draw a causal diagram that reflects her qualitative belief—or, better yet, the consensus belief of researchers in her field of expertise—about the topology of the causal processes at work. She must abandon the centuries-old dogma of objectivity for objectivity’s sake. Where causation is concerned, a grain of wise subjectivity tells us more about the real world than any amount of objectivity.

Linguistic barriers are not surmounted so easily.

Indeed, observe the rich content of this short sentence of instructions.

When we start talking about strong AI, causal models move from a luxury to a necessity. To me, a strong AI should be a machine that can reflect on its actions and learn from past mistakes.

The ability to conceive of one’s own intent and then use it as a piece of evidence in causal reasoning is a level of self-awareness (if not consciousness) that no machine I know of has achieved. I would like to be able to lead a machine into temptation and have it say, “No.”

think that understanding the benefits of the illusion of free will is the key

to the stubbornly enigmatic problem of reconciling it with determinism. The problem will dissolve before our eyes once we endow a deterministic machine with the same benefits.