Quotes from Judea Pearl

If I could sum up the message of this book in one pithy phrase, it would be that you are smarter than your data. Data do not understand causes and effects; humans do.

You cannot answer a question that you cannot ask, and you cannot ask a question that you have no words for.

Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can't tell you why. Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.

while probabilities encode our beliefs about a static world, causality tells us whether and how probabilities change when the world changes, be it by intervention or by act of imagination.

the surest kind of knowledge is what you construct yourself.

Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality.

Where causation is concerned, a grain of wise subjectivity tells us more about the real world than any amount of objectivity.

My emphasis on language also comes from a deep conviction that language shapes our thoughts. You cannot answer a question that you cannot ask, and you cannot ask a question that you have no words for.

you are smarter than your data. Data do not understand causes and effects; humans do.

How much evidence would it take to convince us that something we consider improbable has actually happened? When does a hypothesis cross the line from impossibility to improbability and even to probability or virtual certainty?

skepticism has its place. Statisticians are paid to be skeptics; they are the conscience of science.

I conjecture, that human intuition is organized around casual, not statistical, relations.

The two fundamental questions of causality are: (1) What empirical evidence is required for legitimate inference of cause–effect relationships? (2) Given that we are willing to accept causal information about a phenomenon, what inferences can we draw from such information, and how?

Counterfactual reasoning, which deals with what-ifs, might strike some readers as unscientific. Indeed, empirical observation can never confirm or refute the answers to such questions.

I would rather discover one cause than be the King of Persia.

Despite heroic efforts by the geneticist Sewall Wright (1889–1988), causal vocabulary was virtually prohibited for more than half a century. And when you prohibit speech, you prohibit thought and stifle principles, methods, and tools.

Fighting for the acceptance of Bayesian networks in AI was a picnic compared with the fight I had to wage for causal diagrams [in the stormy waters of statistics].

The problem with monotonic logic lies not in the hardness of its truth values, but rather in its inability to process context-dependent information.

Another feature we lose in going from logic to uncertainty is incrementality.

Much of this data-centric history still haunts us today. We live in an era that presumes Big Data to be the solution to all our problems. Courses in "data science" are proliferating in our universities, and jobs for "data scientists" are lucrative in the companies that participate in the "data economy." But I hope with this book to convince you that data are profoundly dumb.

Counterfactuals are the building blocks of moral behavior as well as scientific thought. The ability to reflect on one's past actions and envision alternative scenarios is the basis of free will and social responsibility. The algorithmization of counterfactuals invites thinking machines to benefit from this ability and participate in this (until now) uniquely human way of thinking about the world.

Presumably the child brain is something like a notebook as one buys it from the stationer's," he wrote. "Rather little mechanism, and lots of blank sheets.

definitions demand reduction, and reduction demands going to a lower rung.

To show what is still needed, let us examine how an ideal system might reason about the burglar alarm situation of Figure 1.2. Upon receiving the phone call from your neighbor, only the burglary hypothesis is triggered; your decision whether to drive home or stay at work is made solely on the basis of the parameter P(False alarm), which summarizes all other (unexplicated) causes for an alarm sound. After a moment's reflection, the possibility of an April Fools' Day joke may