The Book of Why: The New Science of Cause and Effect
by Judea Pearl
In "The Book of Why: The New Science of Cause and Effect," Judea Pearl argues for a paradigm shift in how we understand and engage with data, emphasizing the importance of causality over mere correlation. Central to his thesis is the assertion that "you are smarter than your data," highlighting that while data can provide insights, they lack the ability to inherently grasp causes and effects. Pearl advocates for the use of counterfactual reasoning,considering "what-ifs" to explore alternative scenarios,as a crucial tool for human thought and scientific inquiry. The book critiques the prevailing reliance on data-centric approaches, warning that such methods can be misleading without a proper causal framework. Pearl asserts that our understanding of the world requires a vocabulary of causation, which has historically been underappreciated in scientific discourse. He posits that human intuition is inherently linked to causal relationships, which should guide our interpretations of data. Moreover, Pearl addresses the challenges of confounding variables and the limitations of statistical models that fail to account for underlying causal mechanisms. He underscores the necessity of refining our models based on available scientific knowledge to answer causal questions effectively. Ultimately, Pearl champions a deeper understanding of causality as essential to moral reasoning, scientific advancement, and the development of intelligent machines capable of comprehending the complexities of reality.
18 popular highlights from this book
Key Insights & Memorable Quotes
Below are the most popular and impactful highlights and quotes from 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.
you are smarter than your data. Data do not understand causes and effects; humans do.
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.
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.
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.
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.
the surest kind of knowledge is what you construct yourself.
Where causation is concerned, a grain of wise subjectivity tells us more about the real world than any amount of objectivity.
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.
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.
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].
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.
