CASUALITY MODELS REASONING AND INTERFERENCE PEARL J. Księgarnia

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

Very interdisciplinary, will appeal to scholars in many fields
Shows how economics, social science, epidemiology, and statistics can be taught as studies in cause and effect
Students will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts
Judea Pearl is very well-known and the name will sell the book

Table of Contents

1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpsons paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.

Reviews

Without assuming much beyond elementary probability theory. Judea Pearls book provides an attractive tour of recent work, in which he has played a central role, on causal models and causal reasoning. Due to his efforts, and that of a few others, a Renaissance in thinking and using causal concepts is taking place.
Patrick Suppes, Center for the Study of Language and Information, Stanford University

Judea Pearl has come to statistics and causation with enthusiasm and creativity. his work is always thought provoking and worth careful study. This book proves to be no exception. Time and again I found myself disagreeing both with his assumptions and with his conclusions, but I was also fascinated by new insights into problems I thought I already understood well. This book illustrates the rich contributions Pearl has made to the statistical literature and to our collective understanding of models for causal reasoning.
Stephen Fienberg, Maurice Falk University professor of Statistics and Social Science, Carnegie Mellon University

The book is extremely well written, and while mathematically precise, provides a thought-provoking study of causality and its implications.
Computing Review

384 pages

Po otrzymaniu zamówienia poinformujemy,
czy wybrany tytuł polskojęzyczny lub anglojęzyczny jest aktualnie na półce księgarni.