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New PDF release: The Art of Causal Conjecture

By Glenn Shafer

ISBN-10: 026219368X

ISBN-13: 9780262193689

Within the artwork of Causal Conjecture, Glenn Shafer lays out a brand new mathematical and philosophical starting place for chance and makes use of it to provide an explanation for options of causality utilized in information, synthetic intelligence, and philosophy.The a number of disciplines that use causal reasoning range within the relative weight they wear defense and precision of information in place of timeliness of motion. The common and social sciences search excessive degrees of walk in the park within the identity of reasons and excessive degrees of precision within the size in their results. the sensible sciences--medicine, company, engineering, and synthetic intelligence--must act on causal conjectures in response to extra constrained wisdom. Shafer's figuring out of causality contributes to either one of those makes use of of causal reasoning. His language for causal clarification can advisor statistical research within the average and social sciences, and it may possibly even be used to formulate assumptions of causal uniformity wanted for choice making within the functional sciences.Causal principles permeate using chance and facts in all branches of undefined, trade, executive, and technology. The artwork of Causal Conjecture exhibits that causal rules should be both vital in concept. It doesn't problem the maxim that causation can't be confirmed from facts by myself, yet through bringing causal rules into the principles of likelihood, it permits causal conjectures to be extra in actual fact quantified, debated, and faced via statistical facts.

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Extra resources for The Art of Causal Conjecture

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A few readers may be ready for a first perusal of these chapters immediately after completing this introduction. Most readers will need first to deepen their mastery of probability­ tree concepts, through an exploration of parts of Chapters 2 through 1 3 . Some may wish even to cover these intermediate chapters thoroughly on a first read­ ing. However quickly they reach the later chapters, I hope that all readers will take it as their goal to master and develop the ideas on causal explanation and causal modeling adumbrated there.

We call this the standard filter. . . . - . • • . 6. Thi s term goes back to Thomas Bayes's famous essay, published posthumously in l764 and reprinted by Pearson and Kendall (1970). For a discussion of how Bayes's language and arguments can be understood in terms of event trees, see Shafer (1982). 43 Event Trees Although it is sometimes useful, it is not as basic as it might first appear; as we will see in Chapter 13, we can refine event trees by interpolating more detailed situations along some branches but not others, and this changes the way situa­ tions are grouped to form the cuts in the standard filter.

Following the established practice of probabilists and statisticians, we have been calling subsets of the sample space events. This sense of "event" was already prominent in Abraham De M oivre's book The Doctrine of Chances ( 1 7 18), and hence we may call it the Moivrean sense. But there is another sense, more common in everyday language and often favored by philosophers. An event in this sense is something localized 24 Chapter 1 in time and space, perhaps a change in some object or circumstance at a particu­ lar time.

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The Art of Causal Conjecture by Glenn Shafer


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