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. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 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 interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
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