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

Philip was the first economist to make use of his son’s invention of path diagrams.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
language. Linguistic barriers are not surmounted so easily.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
Around 1923 or 1924, Fisher began to realize that the only experimental design that the genie could not defeat was a random one.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
having such laws permits us to violate them selectively so as to create worlds
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
.modelthinking interestinh thought that create laws and violate them selectively when required
Counterfactual learners, on the top rung, can imagine worlds that do not exist and infer reasons for observed phenomena.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
Harari posits that our ancestors’ capacity to imagine nonexistent things was the key to everything, for it allowed them to communicate better.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
Statisticians have been immensely confused about what variables should and should not be controlled for, so the default practice has been to control for everything one can measure. The vast majority of studies conducted in this day and age subscribe to this practice. It is a convenient, simple procedure to follow, but it is both wasteful and ridden
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The rewards of having a causal model that can answer counterfactual questions are immense. Finding out why a blunder occurred allows us to take the right corrective measures in the future. Finding out why a treatment worked on some people and not on others can lead to a new cure for a disease. Answering the question “What if things had been differe
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.modelthinking counterfactuals seems to create data dependent alternate scenarios which can tell us where we can improve the process
A confounder will make A and C statistically correlated even though there is no direct causal link between them.