TY  - GEN
N2  - In recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and how causal relations can be learned, at least by computers. The representations used in the emerging theory are causal Bayes nets or graphical causal models. In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. He demonstrates their potential as a powerful tool for guiding experimental inquiry and for interpreting results in developmental psychology, cognitive neuropsychology, psychometrics, social psychology, and studies of adult judgment. Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted - without the benefit of Bayes nets and graphical causal models. The capstone illustration is an analysis of the methods used in Herrnstein and Murray's book The Bell Curve ; Glymour argues that new, more reliable methods of data analysis, based on Bayes nets representations, would lead to very different conclusions from those advocated by Herrnstein and Murray.
AB  - In recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and how causal relations can be learned, at least by computers. The representations used in the emerging theory are causal Bayes nets or graphical causal models. In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. He demonstrates their potential as a powerful tool for guiding experimental inquiry and for interpreting results in developmental psychology, cognitive neuropsychology, psychometrics, social psychology, and studies of adult judgment. Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted - without the benefit of Bayes nets and graphical causal models. The capstone illustration is an analysis of the methods used in Herrnstein and Murray's book The Bell Curve ; Glymour argues that new, more reliable methods of data analysis, based on Bayes nets representations, would lead to very different conclusions from those advocated by Herrnstein and Murray.
T1  - The mind's arrows :Bayes nets and graphical causal models in psychology /
DA  - ©2001.
CY  - Cambridge, Mass. :
AU  - Glymour, Clark N.
CN  - BF38.5
PB  - MIT Press,
PP  - Cambridge, Mass. :
PY  - ©2001.
N1  - "A Bradford book."
ID  - 1387396
KW  - Psychology
KW  - Prediction theory.
KW  - Causation.
KW  - COGNITIVE SCIENCES/General
KW  - COGNITIVE SCIENCES/Psychology/Cognitive Psychology
SN  - 9780262273961
SN  - 0262273969
SN  - 9780262318730
SN  - 0262318733
SN  - 9780262072205
SN  - 0262072203
SN  - 058547544X
SN  - 9780585475448
TI  - The mind's arrows :Bayes nets and graphical causal models in psychology /
LK  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4638.001.0001?locatt=mode:legacy
LK  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
UR  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4638.001.0001?locatt=mode:legacy
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -