TY  - GEN
AB  - "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."
AU  - Shafer, Glenn,
CN  - Q335
CY  - Cambridge, Mass. :
DA  - ©1996.
ID  - 1412361
KW  - Artificial intelligence.
KW  - Causation.
KW  - Prediction (Logic)
KW  - Probabilities.
KW  - COMPUTER SCIENCE/General
LK  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/1403.001.0001?locatt=mode:legacy
LK  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
N2  - "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."
PB  - MIT Press,
PP  - Cambridge, Mass. :
PY  - ©1996.
SN  - 0262290995
SN  - 9780262290999
T1  - The art of causal conjecture /
TI  - The art of causal conjecture /
UR  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/1403.001.0001?locatt=mode:legacy
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -