CausalityCambridge University Press, 14 sept 2009 - 464 páginas 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. |
Índice
Introduction to Probabilities Graphs and Causal Models | 1 |
A Theory of Inferred Causation | 41 |
Causal Diagrams and the Identification of Causal Effects | 65 |
Actions Plans and Direct Effects | 107 |
Causality and Structural Models in Social Science and Economics | 133 |
Simpsons Paradox Confounding and Collapsibility | 173 |
The Logic of StructureBased Counterfactuals | 201 |
Bounding Effects and Counterfactuals | 259 |
Reflections Elaborations and Discussions with Readers | 331 |
4 | 354 |
5 | 365 |
6 | 380 |
7 | 389 |
Epilogue The Art and Science of Cause and Effect | 401 |
Bibliography | 429 |
| 454 | |
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Términos y frases comunes
actions adjustment analysis arrows Artificial Intelligence associated assume axioms back-door criterion back-door paths Bayesian networks causal assumptions causal diagram causal effect causal inference causal model causal relationships cause Chapter coefficients compute concepts conditional independence conditional probability confounding correlation counterfactual covariates d-separation decision decision trees defined Definition dependent direct effect do(x econometrics equivalent estimate evaluation event example exogeneity experimental expression factors Figure formal function given graphical Greenland Heckman identifiable implies instrumental variables interpretation intervention intuition joint distribution Judea Pearl latent linear manipulations Markov Markovian mathematical measure mechanisms nodes notation notion observed outcome P(y ƒ P(yx parameters parents Pearl potential-outcome predict probabilistic problem propensity score quantities randomized represents Robins Rubin Section semantics set of variables Simpson's paradox specific statistical structural equation models structural model studies subset sufficient Theorem theory tion treatment U₁ X₁ Yx(u Z₁