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21 " I hope with this book to convince you that data are profoundly dumb. 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. "
― , The Book of Why: The New Science of Cause and Effect
22 " paradox is more than that: it should entail a conflict between two deeply held convictions. "
23 " Scientists should seek shielded mediators whenever they face incurable confounders. "
24 " I conjecture, that human intuition is organized around casual, not statistical, relations. "
25 " With Bayesian networks, we had taught machines to think in shades of grey, 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.... Without the ability to envision alternate realities and contrast them with the currently existing reality, a machine...cannot answer the most basic question that makes us human: "Why? "
26 " 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. "
27 " 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. "
28 " [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. "
29 " 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. "
30 " I would rather discover one cause than be the King of Persia. "