The art of causal conjecture
The art of causal conjecture
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DOI：
10.2307/2670064
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
G. Shafer
中科院分区：
文献类型：
--
作者：
G. Shafer
从出版商那里：“因果关系在许多领域都起着重要作用，从工程到医学再到人工智能。格伦·谢弗（Glenn Shafer）撰写了一项重要的，学术研究的因果关系。他从数学上和哲学上扎实的概率基础开始，使用了数学和哲学上的固体基础，使用它仔细区分因果关系的观念 - 所有这些都在文献中发挥了重要作用，并表明了如何从证据中发现每个人。对统计和哲学的基本问题以及因果关系的实际应用感兴趣。” - 康奈尔大学因果猜想的康奈尔大学计算机科学系教授约瑟夫·霍尔珀（Joseph Y.哲学。使用因果推理的各种学科在其对安全性和知识精确的相对权重而不是及时性方面的相对重量方面有所不同。自然和社会科学在识别原因和高度精确度的效果时寻求高水平的确定性。实际科学 - 医学，商业，工程和人工智能 - 必须基于更多有限知识的因果猜想。 Shafer对因果关系的理解有助于这两种因果推理的用途。他的因果解释语言可以指导自然科学和社会科学的统计调查，也可以用于制定实践科学决策所需的因果统一的假设。因果观念渗透到行业，商业，政府和科学的所有分支中的概率和统计数据。因果猜想的艺术表明，因果观念在理论上同样重要。并非挑战因果关系不能仅凭统计来证明，而是通过将因果观念带入概率的基础上，它允许因果猜想得到更清楚地量化，辩论和与统计证据面对面的原因。
From the Publisher: "Causality plays an important role in many fields, from engineering to medicine to artificial intelligence. Glenn Shafer has written an important, scholarly study of causality. He starts with a novel foundation for probability that is mathematically and philosophically solid, uses it to distinguish carefully between notions of causality -- all of which have played an important role in the literature -- and shows how each can be discovered from evidence. This is a book that will be of interest to those interested in foundational questions of statistics and philosophy, as well as in practical applications of causality." -- Joseph Y. Halpern, Professor, Computer Science Department, Cornell University In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences -- medicine, business, engineering, and artificial intelligence -- must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.