Causality: Models, Reasoning, and InferenceWritten 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, philosophy, cognitive science, and the health and social sciences. 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, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students 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. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science. 
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Review: Causality: Models, Reasoning, and Inference
User Review  Delhi Irc  GoodreadsLocation: GG6 IRC Accession no: DL026784 Read full review
Review: Causality: Models, Reasoning, and Inference
User Review  GoodreadsLocation: GG6 IRC Accession no: DL026784 Read full review
Contents
II  1 
III  2 
IV  6 
V  8 
VI  11 
VII  12 
VIII  13 
IX  16 
LXXXVII  173 
LXXXVIII  174 
XC  175 
XCI  177 
XCII  180 
XCIII  182 
XCIV  184 
XCV  185 
X  20 
XI  21 
XII  22 
XIII  24 
XIV  26 
XV  27 
XVI  30 
XVII  32 
XVIII  33 
XIX  38 
XX  41 
XXI  42 
XXII  43 
XXIII  45 
XXIV  48 
XXV  49 
XXVI  51 
XXVII  54 
XXVIII  57 
XXIX  59 
XXX  61 
XXXI  65 
XXXII  66 
XXXIII  68 
XXXIV  70 
XXXV  72 
XXXVI  77 
XXXVII  78 
XXXVIII  79 
XXXIX  81 
XL  83 
XLI  85 
XLIV  86 
XLV  88 
XLVI  89 
XLVII  91 
XLVIII  93 
XLIX  94 
L  96 
LI  98 
LII  102 
LIII  107 
LIV  108 
LV  110 
LVI  112 
LVII  113 
LVIII  114 
LX  116 
LXI  117 
LXII  118 
LXV  120 
LXVI  121 
LXVII  124 
LXVIII  126 
LXIX  127 
LXX  128 
LXXI  130 
LXXII  133 
LXXIII  134 
LXXIV  135 
LXXV  138 
LXXVI  140 
LXXVIII  144 
LXXIX  145 
LXXX  149 
LXXXI  154 
LXXXII  157 
LXXXIII  159 
LXXXIV  163 
LXXXV  165 
LXXXVI  170 
XCVII  186 
XCIX  188 
C  189 
CI  191 
CII  192 
CIII  193 
CIV  194 
CV  196 
CVI  199 
CVII  201 
CVIII  202 
CX  207 
CXI  212 
CXII  213 
CXIII  215 
CXIV  217 
CXV  221 
CXVI  223 
CXVII  226 
CXVIII  228 
CXX  231 
CXXI  234 
CXXII  238 
CXXIII  240 
CXXIV  242 
CXXV  243 
CXXVI  245 
CXXVII  249 
CXXIX  250 
CXXX  252 
CXXXI  253 
CXXXII  256 
CXXXIII  259 
CXXXIV  261 
CXXXV  262 
CXXXVI  263 
CXXXVII  266 
CXXXVIII  268 
CXXXIX  269 
CXL  270 
CXLI  271 
CXLII  274 
CXLIII  275 
CXLIV  277 
CXLVI  280 
CXLVII  281 
CXLVIII  283 
CXLIX  286 
CL  289 
CLI  291 
CLII  293 
CLIII  296 
CLIV  297 
CLV  299 
CLVI  302 
CLVII  307 
CLVIII  309 
CLIX  311 
CLX  313 
CLXII  316 
CLXIII  318 
CLXIV  320 
CLXV  322 
CLXVI  324 
CLXVII  325 
CLXVIII  327 
CLXIX  331 
CLXX  359 
375  
379  
Common terms and phrases
ACE(X actions algebraic algorithm analysis arrows Artificial Intelligence associated assumptions axioms backdoor criterion backdoor paths Bayesian networks calculus causal diagram causal effect causal inference causal model causal relationships Chapter coefficients compute concepts conditional independence conditional probability confounding consider correlation counterfactual covariance dseparated defined Definition dependent derived direct effect directed acyclic graph do(x econometrics equivalent estimate evaluation event example exogeneity experimental explanation expression factors Figure formal given graph graphical Greenland hypothetical identifiable implies instrumental variables interpretation intervention joint distribution Judea Pearl linear logic Markov Markovian mathematical measure mechanisms minimal nodes notion observed variables obtain P(yx parameters parents path coefficients Pearl potentialoutcome predict probabilistic causality problem quantities query random represents Robins rules Science Section semantics set of variables Simpson's paradox specific Spirtes statistical structural equation models structural model subset sufficient Theorem theory tion treatment unobserved Yx(u