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An introduction to causal inference in neuroimaging

Published on Apr 03, 20143696 Views

Moritz Grosse-Wentrup

A variety of causal inference methods has been introduced to neuroimaging in recent years, including Causal Bayesian Networks, Dynamic Causal Modeling (DCM), Granger Causality, and Linear Non-Gaussian

BBCI Winter School 2014 - Berlin

Related categories

MedicineNeurotechnology

Chapter list

An Introduction to Causal Inference in Neuroimaging00:00
Why should we be interested in causal inference? - 101:14
Why should we be interested in causal inference? - 201:55
Why should we be interested in causal inference? - 301:59
Why should we be interested in causal inference? - 402:18
Why should we be interested in causal inference? - 502:42
Why should we be interested in causal inference? - 602:55
Why should we be interested in causal inference? - 703:09
The potential outcomes framework - 104:39
The potential outcomes framework - 205:43
The potential outcomes framework - 305:52
The potential outcomes framework - 405:57
The potential outcomes framework - 506:03
The potential outcomes framework - 606:07
The potential outcomes framework - 706:11
The potential outcomes framework - 806:24
The potential outcomes framework - 906:37
The potential outcomes framework - 1006:47
The potential outcomes framework - 1106:54
The potential outcomes framework - 1206:58
The potential outcomes framework - 1307:21
The potential outcomes framework - 1407:40
The potential outcomes framework - 1507:43
The potential outcomes framework - 1608:02
The potential outcomes framework - 1708:25
The potential outcomes framework - 1808:59
The potential outcomes framework - 1910:14
The potential outcomes framework - 2010:32
The potential outcomes framework - 2111:05
The potential outcomes framework - 2211:33
The potential outcomes framework - 2311:41
The potential outcomes framework - 2412:06
The potential outcomes framework - 2512:19
The potential outcomes framework - 2612:34
The potential outcomes framework - 2712:59
Causal inference in neuroimaging - 113:36
Causal inference in neuroimaging - 213:40
Causal inference in neuroimaging - 313:53
Causal inference in neuroimaging - 414:04
Causal inference in neuroimaging - 514:24
Causal inference in neuroimaging - 614:37
Causal inference in neuroimaging - 714:53
Causal inference in neuroimaging - 815:14
Causal inference in neuroimaging - 915:30
Outline15:46
Outline: Granger Causality17:10
Granger Causality - 117:13
Granger Causality - 217:46
Granger Causality - 318:35
Granger Causality - 418:42
Granger Causality - 519:14
Granger Causality - 619:33
Granger causality: Confounding - 120:39
Granger causality: Confounding - 221:03
Granger causality: Confounding - 322:21
Granger causality: Confounding - 422:25
Granger causality: Confounding - 522:39
Granger causality: Confounding - 625:09
Granger causality: Confounding - 727:04
Granger causality: Directed transfer function (DTF) - 127:07
Granger causality: Directed transfer function (DTF) - 227:26
Granger causality: Directed transfer function (DTF) - 327:37
Granger causality: Directed transfer function (DTF) - 427:58
Granger causality: Directed transfer function (DTF) - 528:19
Granger causality: Directed transfer function (DTF) - 628:33
Granger causality: Directed transfer function (DTF) - 728:42
Granger causality: Directed transfer function (DTF) - 828:51
Granger causality: Directed transfer function (DTF) - 929:06
Granger causality: Case study29:29
Causal inference in neuroimaging - 133:12
Causal inference in neuroimaging - 233:29
Causal inference in neuroimaging - 333:38
Causal inference in neuroimaging - 433:50
Outline: Causal Bayesian Networks34:04
Causal Bayesian Networks: Introductory example - 134:10
Causal Bayesian Networks: Introductory example - 235:41
Causal Bayesian Networks: Introductory example - 335:43
Causal Bayesian Networks: Introductory example - 436:04
Causal Bayesian Networks: Introductory example - 536:31
Causal Bayesian Networks: Introductory example - 637:19
Causal Bayesian Networks: Introductory example - 737:31
Causal Bayesian Networks: Introductory example - 837:47
Causal Bayesian Networks: Introductory example - 938:07
Causal Bayesian Networks: Potential causation - 139:30
Causal Bayesian Networks: Potential causation - 240:24
Causal Bayesian Networks: Spurious association - 241:52
Causal Bayesian Networks: Spurious association - 342:10
Causal Bayesian Networks: Spurious association - 142:17
Causal Bayesian Networks: Genuine causation42:34
Causal Bayesian Networks: Predicting interventions - 146:58
Causal Bayesian Networks: Predicting interventions - 247:48
Causal Bayesian Networks: Predicting interventions - 347:56
Causal Bayesian Networks: Predicting interventions - 448:29
Causal Bayesian Networks: Predicting interventions - 548:34
Causal Bayesian Networks: Predicting interventions - 649:43
Causal Bayesian Networks: Predicting interventions - 749:51
Causal Bayesian Networks: Faithfulness - 150:41
Causal Bayesian Networks: Faithfulness - 252:46
Causal Bayesian Networks: Faithfulness - 352:52
Causal Bayesian Networks: Faithfulness - 453:45
Causal Bayesian Networks: Conditional independence tests - 156:39
Causal Bayesian Networks: Conditional independence tests - 257:02
Causal Bayesian Networks: Conditional independence tests - 357:05
Causal Bayesian Networks: Conditional independence tests - 457:32
Causal Bayesian Networks: Conditional independence tests - 557:59
Causal Bayesian Networks: Conditional independence tests - 658:23
Causal Bayesian Networks: Case study - 159:45
Causal Bayesian Networks: Case study - 201:00:35
Causal Bayesian Networks: Case study - 301:01:18
Causal Bayesian Networks: Case study - 401:02:32
Causal inference in neuroimaging - 501:05:35
Causal inference in neuroimaging - 601:05:40
Causal inference in neuroimaging - 701:05:47
Causal inference in neuroimaging - 801:05:53
Causal inference in neuroimaging - 901:06:04
Outline: Dynamic Causal Modelling01:06:32
Dynamic Causal Modelling - 101:06:58
Dynamic Causal Modelling - 201:07:52
Dynamic Causal Modelling - 301:07:57
Dynamic Causal Modelling - 401:08:16
Dynamic Causal Modelling - 501:08:59
Dynamic Causal Modelling - 601:09:11
Dynamic causal modelling: The hemodynamic model01:16:22
Dynamic causal modelling: The bilinear model01:16:27
Dynamic causal modelling: Model comparison01:17:00
Dynamic causal modelling - 101:17:59
Dynamic causal modelling - 201:19:50
Dynamic causal modelling: Model fit & structure similarity - 101:20:12
Dynamic causal modelling: Model fit & structure similarity - 201:21:08
Dynamic causal modelling: Model fit & structure similarity - 301:22:18
Dynamic causal modelling: Model fit & structure similarity - 401:22:52
Causal inference in neuroimaging - 1001:27:00
Causal inference in neuroimaging - 1101:27:01
Causal inference in neuroimaging - 1201:27:05
Causal inference in neuroimaging - 1301:27:25
Causal inference in neuroimaging - 1401:27:31
Outline: Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM)01:27:36
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 101:27:43
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 201:29:41
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 301:29:58
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 401:30:31
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 501:31:15
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 601:31:38
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 701:31:39
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 801:31:42
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 901:31:54
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 1001:32:03
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 1101:32:09
Non-Linear Non-Gaussian Acyclic Models (Non-LiNGAM) - 1201:32:13
Causal inference in neuroimaging - 1501:33:37
Causal inference in neuroimaging - 1601:33:41
Causal inference in neuroimaging - 1701:33:52
Causal inference in neuroimaging - 1801:33:58
Outline: Summary01:34:10
Empirical Performance01:34:16
Causal inference in neuroimaging - 1901:36:23
Causal inference in neuroimaging - 2001:36:24
Conclusions - 101:36:31
Conclusions - 201:36:36
Conclusions - 301:37:53
Conclusions - 401:38:02
Conclusions - 501:38:06
Conclusions - 601:39:20
Conclusions - 701:39:37
4th Int. Workshop on Pattern Recognition in Neuroimaging (PRNI 2014)01:40:02