Cambridge University Press, 14 sep. 2009
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
Comentarios de usuarios - Escribir una reseña
No hemos encontrado ninguna reseña en los sitios habituales.
Otras ediciones - Ver todo
actions adjustment algebraic analysis arrows Artiﬁcial Intelligence associated assume axioms back-door criterion back-door paths Bayesian networks causal assumptions causal diagram causal effect causal model causal relationships cause Chapter coefﬁcients compute concepts conditional independence conditional probabilities confounding correlation counterfactual covariates d-separation decision trees deﬁned Deﬁnition dependent difﬁculties direct effect do(X I econometrics equivalent estimate evaluation event example exogeneity experimental expression factors Figure ﬁnd ﬁre ﬁrst formal function given graph graphical Greenland I P(y identiﬁable implies inference inﬂuence instrumental variables interpretation intervention intuition joint distribution Judea Pearl latent linear manipulations Markov mathematical measure mechanisms modiﬁed nodes notation notion observed P(yx parameters parents Pearl potential-outcome predict probabilistic problem propensity score quantities randomized represents Robins Rubin satisﬁes scientiﬁc Section semantics set of variables speciﬁc statistical structural equation models structural model subset sufﬁcient Theorem theory tion treatment Yx(u