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Shafer G. — The Art of Causal Conjecture
Shafer G. — The Art of Causal Conjecture

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Название: The Art of Causal Conjecture

Автор: Shafer G.

Аннотация:

In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences — medicine, business, engineering, and artificial intelligence — must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.


Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1996

Количество страниц: 511

Добавлена в каталог: 22.10.2010

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
accounting for      429
Adams, W.J.      406
Affect      see "To affect"
Algebra      396
Analysis of variance      see "Partition of variance and covariance"
Ancestor      233 386
Andersen, Per Kragh      253
Arntzenius, Frank      153
Assemblage      477
Asymmetry      393
Attribution      316
Axiom(s) for Doob catalogs      262
Axiom(s) for event trees      233
Axiom(s) for independence      186—188 450—452
Axiom(s) for lifting      284—285
Axiom(s) for martingales      79 257
Axiom(s) for probability catalogs      81
Axiom(s) for probability in event trees      68
Axiom(s) for probability measures      399
Axiom(s) for refining      283
Axiom(s) for regular event trees      240—241
Axiom(s) for simplifying      285
Axiom(s) of additivity      399
Axiom(s), intersection      452
Bar-Hillel, M.      111
Baron, Reuben M.      317
Barwise, Jon      375
Bayes net      366—368
Bayes, Thomas, event tree reasoning      61 62
Bayes, Thomas, independence      133
Bayes, Thomas, subsequent events      42
Bayesian reasoning as analogy      110
Bayesian reasoning, use of sample-space framework      91—92
Belnap, Nuel, intersecting event tree      60
Belnap, Nuel, next situation      241
Bennett, Jonathan      43
Bentler, Peter      462
Bernardo, Jose M.      110
Bernert, Christopher      357
Bernoulli, Jacob, gambler's ruin      380
Bernoulli, Jacob, law of large numbers      406
Bernoulli, Jacob, maxims      300
Bernoulli, Jacob, subjectivity      1 105
Binary relation      393
Birkhoff, Garrett      393
Blalock, H.M.      456
Bollen, Kenneth A.      454 464
Borchardt, Gary C.      378
Boudon, Raymond      456
Branching probabilities      5 69 265
Breslow, N.E.      330
Bryant, Randal      363
Buntine, Wray L.      360
Canonical event tree      55
Cartwright, Nancy      133
Catalog      261 see
Catalog, completion of      267
Catalog, Doob      262
Catalog, fair-bet      262
Causal conjecture, distinguished from proof      15
Causal conjecture, maxims for      301
Causal diagrams, independence      341
Causal diagrams, joint      20—23 339—342
Causal diagrams, linear-sign      339
Causal diagrams, mean tracking      339
Causal diagrams, singular      147—149
Causal diagrams, strong tracking      339
Causal diagrams, uncorrelatedness      341
Causal diagrams, unpredictability      341
Causal explanation, diversity of      302
Causal explanation, purpose of      304
Causal interpretation of conditional independence      16
Causal interpretation of partial uncorrelatedness      16
Causal interpretation of regression coefficient      16 313 314
Causal interpretation of statistical prediction      334—335
Causal law as rule governing intervention      27
Causal law as rule of construction      108
Causal law in type theory      377—378
Causal models      331—357
Causal relations among Moivrean events      10
Causal relations among Moivrean variables      11—12
Cause      see also "Common causes" "Effect"
Cause, average effect of      17—19 305—311
Cause, dynamic nature of      14
Cause, misuse of the word      13 18—19 160—162 304 336
Chain      385
Chance situation      249 265
Change in belief      101—106
Charniak, Eugene      369
Chebyshev's inequality      405
Child      386
Coarsening      397
Collider      388 457
Colon, use in definitions      41
Common causes, basis of causal relations      12—13
Common causes, dimensionality of      353—355
Common causes, partial and complete      149—151
Common causes, source of correlation      121—128
Comparative evidence      15—16 322 343
Complement      43
Concomitant      314
Concomitant, cut      54 246 313
Concomitant, Moivrean event as      18
Concomitant, Moivrean variable as      54
Conditional distribution for prediction      333
Conditional distribution in sample space      411
Conditional expectation      64 87 410 412
Conditional expected value as tracking function      205
Conditional expected value in probability tree      64 87
Conditional expected value in sample space      407
Conditional independence and tracking      142—143 190—192
Conditional independence for Moivrean events      128—133
Conditional independence for Moivrean variables      175—177
Conditional independence in sample space      431
Conditional probability as revised belief      102
Conditional probability as tracking probability      142 194
Conditional probability in probability tree      64 73
Conditional probability in sample space      406
Conditional probability, novelty of      104
Configuration in assemblage      477
Configuration in event tree      52
Configuration in sample space      401
Conjunctive fork      150
Constable, Robert L.      371 374
Construction of probability tree      108 360
Construction ordering      341 387
Construction ordering, not needed for independence and uncorrelatedness diagrams      341
Construction ordering, not needed for Markov and linear relevance diagram      468
Cornfield, Jerome      336
Correlation coefficient      14 438
Correlation coefficient and regression coefficient      438
Correlation coefficient in a situation      79
Cost      270
Counterfactual      108
Covariance      77 403
Covariance structure model      462
Covariance structure model, recursive      464
Covariate      315
Cox, D.R.      253 329 330
Cross-sectional evidence      322 see
Cut      39 235
Cut of situation      41 235
Cut, daughter      41
Cut, expectation in      83
Cut, identification of      51 53
Cut, initial      40
Cut, lattice of cuts      40 238—239
Cut, ordered with situation      237
Cut, partial      41 235
Cut, proper      40
Cut, resolving      41 245
Cut, terminal      40
d-connection      388
D-Separation      341 388
daughter      34 233 386
Davey, B.A.      393
Dawid's axioms      186—188 341 450—452 469
Dawid, A.P., axioms      187 450 452
Dawid, A.P., Jeffreys's law      406
Dawid, A.P., long-run aspects of probability      111
Dawid, A.P., sequential prediction      107—108
Day, N.E.      330
De Moivre, Abraham, concept of event      23
De Moivre, Abraham, event-tree reasoning      61
De Moivre, Abraham, gambler's ruin      380
De Moivre, Abraham, varying probability tree      104
De Mori, Renato      369
Decision      360
Decision situation      249 265
Decision tree      62 249—253
Dedekind cut      235
Dempster, A.P.      330 369
Dependence diagram      366 480
Descartes, Rene      380
Descendant      233 386
Determinacy for family of Moivrean variables      53
Determinacy for Moivrean events      37
Determinacy for Moivrean variables      50 244
Determinacy for partitions      52
Determinism      72 234
Direct effect      458
Direct effect, causal interpretation      345
Directed acyclic graph      386
Directed acyclic graph, collider      388 457
Directed acyclic graph, construction ordering for      341 387
Directed acyclic graph, moral graph for      388
Directed acyclic graph, ordered      341 387
Directed chain      386
Directed cycle      386
Directed graph      386—391
Directed path      386
Directed tree      386
Divergence of Humean events      47 246
Divergence of situations      233
Doob closure      262
Doob, J.L.      262 399
Draper, David      17
Druzdzel, Marek      378 472
Duffle, Darrell      62
Edwards, A.W.F.      6 31 62 365 380
Eerola, Mervi      6
Effect in path analysis      345 458
Effect of Humean variable      311—315
Effect of Moivrean event      305—311
Embedding      55 480—482
Empirical relevance      107
Empirical success      106
Endogenous node      386
Endogenous variable      331
Engel, Arthur      365
Engel, Eduardo M.R.A.      32
Engle, R.F.      345
Estimation      13 127—128 360
Ethics of causal talk      160—162 304
Evaluate      see "To evaluate"
Event      see "Humean event" "Moivrean
Event tree      31—62
Event tree as partially ordered set      232—240
Event tree as set of sets      230—232
Event tree for stochastic process      54
Event tree with absolute time scale      57
Event tree with relative time scale      57
Event tree, abstract      229—246
Event tree, intersecting      60
Event tree, regular      240—244
evidence      13—17 109
Evidence, comparative      15—16 322 343
Evidence, longitudinal      13 322 343
Exogenous node      386
Exogenous variable      331
Expectation      412
Expectation as a martingale      267
Expectation expression      413
Expectation in a cut      80 83
Expectation in a situation      64 87 267
Expectation of martingale      80
Expectation, conditional      64 87 410 412
Expectation, interpretation of      95—98
Expectation, lower and upper      269
Expected value      402—405
Expected value in a situation      64 74 267
Expected value, conditional      64 87 410
Expected value, interpretation      92—95
Expected value, lower and upper      269
Experiment as cause      12 121
Experiment in nature's tree      6
Experiment, randomized      109 320—322
Factor      315
Factor analysis      460
failing      37
Fair bet      256 261
Fair-bet catalog      262
Falk, R.      111
Family of Moivrean variables      52—53
Family of Moivrean variables in statistical prediction      337
Family of Moivrean variables, causal relevance      338
Family of Moivrean variables, empty      52
Family of Moivrean variables, identifies cut      53
Family of variables in a sample space      401—402
Family of variables in a sample space, independent      443
Family of variables in a sample space, intersection      401
Family of variables in a sample space, linearly identifies itself      402
Family of variables in a sample space, subfamily      401
Family of variables in a sample space, union      401
Farkas's lemma      264
Fermat, Pierre      379—380
Fetzer, James H.      133
Filter      42 96
Filtration      59 398
Filtration, scaled      60 398
Fisher, Ronald A.      321 330
Forecasting      360
Forecasting system      107
Formal coefficient      223
Formal independence for Moivrean events      115 118
Formal independence for Moivrean variables      171 174
Formal independence, given a Moivrean event      129
Formal independence, posterior to a Moivrean event      129
Formal independence, posterior to a situation      128
Formal score function      226
Formal sign      154 219
Formal sign for families of variables      228
Formal sign, linear      223
Formal sign, scored      226
Formal sign, weak      159
Formal uncorrelatedness      173
Formal uncorrelatedness for more than two Moivrean variables      174
Formal unpredictability in mean      172
Frame for abstract variable      477
Frame for family of variables      52 401
Frame for variable      49 400
Freedman, David A., causal models in social science      357
Freedman, David A., path diagrams      453 460
Freedman, David A., unstandardized coefficients      344 462
frequency      100—101
Freund, John E.      102
Fully informed      296
Functional dependence      203
Functional dependence, weakness as condition      338—340
Gain      96
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