The Art of Causal Conjecture

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MIT Press, 1996 - 511 pagina's
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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.

 

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Inhoudsopgave

Event Trees
31
3
63
4
89
5
113
6
135
7
153
10
215
11
229
Overview
426
Independence Proper
432
Unpredictability in Mean
434
Simple Uncorrelatedness
437
Mixed Uncorrelatedness
438
Partial Uncorrelatedness
440
Independence for Partitions
442
Independence for Families of Variables
445

12
247
13
275
14
299
16
359
Huygenss Probability Trees
379
Some Elements of Graph Theory
385
Some Elements of Order Theory
393
The SampleSpace Framework for Probability
399
Prediction in Probability Spaces
409
Conditional Distribution
411
Regression on a Single Variable
412
Regression on a Partition or a Family of Variables
415
Linear Regression on a Single Variable
418
Linear Regression on a Family of Variables
422
SampleSpace Concepts of Independence
425
The Basic Role of Uncorrelatedness
448
Dawids Axioms
449
Prediction Diagrams
453
Path Diagrams
454
Generalized Path Diagrams
462
Relevance Diagrams
466
Bubbled Relevance Diagrams
475
Abstract Stochastic Processes
477
Abstract Stochastic Processes
479
Embedding Variables and Processes in a Sample Space
480
Glossary of Notation
485
References
491
Index
501
Copyright

Veelvoorkomende woorden en zinsdelen

Populaire passages

Pagina 510 - Crimson, 1990 Representing and Reasoning With Probabilistic Knowledge: A Logical Approach to Probabilities, Fahiem Bacchus, 1 990 3D Model Recognition from Stereoscopic Cues, edited by John EW Mayhew and John P.

Over de auteur (1996)

Glenn Shafer is Professor at Rutgers University's Graduate School of Management.

Bibliografische gegevens