Judea Pearl > Quotes

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“You cannot answer a question that you cannot ask, and you cannot ask a question that you have no words for.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“the surest kind of knowledge is what you construct yourself.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“Where causation is concerned, a grain of wise subjectivity tells us more about the real world than any amount of objectivity.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“you are smarter than your data. Data do not understand causes and effects; humans do.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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?”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“I conjecture, that human intuition is organized around casual, not statistical, relations.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“I would rather discover one cause than be the King of Persia.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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?”
― Judea Pearl, Causality

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“skepticism has its place. Statisticians are paid to be skeptics; they are the conscience of science.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“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].”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“The problem with monotonic logic lies not in the hardness of its truth values, but rather in its inability to process context-dependent information.”
― Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

“Probabilities and the Logic of “Almost True”
― Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

“Another feature we lose in going from logic to uncertainty is incrementality.”
― Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

“overrate it in the sense that they often control for many more variables than they need to and even for variables that they should not”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“How was confounding defined then, and how should it be defined? Armed with what we now know about the logic of causality, the answer to the second question is easier. The quantity we observe is the conditional probability of the outcome given the treatment, P(Y | X). The question we want to ask of Nature has to do with the causal relationship between X and Y, which is captured by the interventional probability P( Y | do(X)). Confounding, then, should simply be defined as anything that leads to a discrepancy between the two: P(Y | X) != P(Y | do(X)). Why all the fuss.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“Scientists should seek shielded mediators whenever they face incurable confounders.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“This brings us to the saddest episode int he whole smoking-cancer controversy: the deliberate efforts of the tobacco companies to deceive the public about the health risks. If Nature is like a genie that answers a question truthfully but only exactly as it is asked, imagine how much more difficult it is for scientists to face an adversary that intends to deceive us. The cigarette wars were science’s first confrontation with organized denialism, and no one was prepared.The tobacco companies magnified any shred of scientific controversy they could. They set up their own Tobacco Industry Research Committee, a front organization that gave money to scientists to study issues related to cancer or tobacco—but somehow never got around to the central question. When they could find legitimate skeptics of the smoking-cancer connection—such as R. A.Fisher and Jacob Yerushalmy—the tobacco companies paid them consulting fees.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“R. A. Fisher, the undisputed high priest of statistics ...”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“[T]he cultural shocks that emanate from new scientific findings are eventually settled by cultural realignments that accommodate those findings—not by concealment. A prerequisite for this realignment is that we sort out the science from the culture before opinions become inflamed.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“enter your mind, in which case a two-stage inference chain is assembled, governed by two probabilistic parameters, P(False alarm) and P(Prank call). Later, when the possibility of an earthquake enters consideration, the parameter P(False alarm) undergoes a partial explication; a fragment of knowledge is brought over from the remote frame of earthquake experiences and is appended to the link Burglary → Alarm as an alternative cause or explanation. The catchall hypothesis All other causes shrinks (to exclude earthquakes), and its parameters are readjusted. The radio announcement strengthens your suspicion in the earthquake hypothesis and permits you to properly readjust your decisions without elaborating the mechanics of the pressure transducer used in the alarm system. The remote possibility of having forgotten to push the reset button will”
― Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

“While we may justly blame Fisher for his obduracy and the tobacco companies for their deliberate deception, we must also acknowledge that the scientific community was laboring in an ideological straightjacket. Fisher had been right to promote randomized controlled trials as a highly effective way to assess a causal effect. However, he and his followers failed to realize that there is much we can learn from observational studies. That is the benefit of a causal model: it leverages the experimenter’s scientific knowledge. Fisher’s methods assume that the experimenter begins with no prior knowledge of or opinions about the hypothesis to be tested. They impose ignorance on the scientist, a situation that the denialists eagerly took advantage of. Because scientists had no straightforward definition of the word “cause” and no way to ascertain a causal effect without a randomized controlled trial, they were ill prepared for a debate over whether smoking caused cancer. They were forced to fumble their way toward a definition in a process that lasted throughout the 1950s and reached a dramatic conclusion in 1964.”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect