Literature DB >> 20970507

Graphical models, potential outcomes and causal inference: comment on Ramsey, Spirtes and Glymour.

Martin A Lindquist1, Michael E Sobel.   

Abstract

Ramsey, Spirtes and Glymour (RSG) critique a method proposed by Neumann et al. (2010) for the discovery of functional networks from fMRI meta-analysis data. We concur with this critique, but are unconvinced that directed graphical models (DGMs) are generally useful for estimating causal effects. We express our reservations using the "potential outcomes" framework for causal inference widely used in statistics.
Copyright © 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20970507      PMCID: PMC4041369          DOI: 10.1016/j.neuroimage.2010.10.020

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  5 in total

1.  On meta-analyses of imaging data and the mixture of records.

Authors:  J D Ramsey; P Spirtes; C Glymour
Journal:  Neuroimage       Date:  2010-08-12       Impact factor: 6.556

2.  Mapping directed influence over the brain using Granger causality and fMRI.

Authors:  Alard Roebroeck; Elia Formisano; Rainer Goebel
Journal:  Neuroimage       Date:  2005-01-12       Impact factor: 6.556

3.  Dynamic causal modelling.

Authors:  K J Friston; L Harrison; W Penny
Journal:  Neuroimage       Date:  2003-08       Impact factor: 6.556

4.  Mapping transcranial magnetic stimulation (TMS) fields in vivo with MRI.

Authors:  D E Bohning; A P Pecheny; C M Epstein; A M Speer; D J Vincent; W Dannels; M S George
Journal:  Neuroreport       Date:  1997-07-28       Impact factor: 1.837

5.  Learning partially directed functional networks from meta-analysis imaging data.

Authors:  Jane Neumann; Peter T Fox; Robert Turner; Gabriele Lohmann
Journal:  Neuroimage       Date:  2009-10-06       Impact factor: 6.556

  5 in total
  9 in total

1.  High-dimensional multivariate mediation with application to neuroimaging data.

Authors:  Oliver Y Chén; Ciprian Crainiceanu; Elizabeth L Ogburn; Brian S Caffo; Tor D Wager; Martin A Lindquist
Journal:  Biostatistics       Date:  2018-04-01       Impact factor: 5.899

2.  Cloak and DAG: a response to the comments on our comment.

Authors:  Martin A Lindquist; Michael E Sobel
Journal:  Neuroimage       Date:  2011-11-17       Impact factor: 6.556

3.  Bayesian network models in brain functional connectivity analysis.

Authors:  Jaime S Ide; Sheng Zhang; Chiang-Shan R Li
Journal:  Int J Approx Reason       Date:  2014-01-01       Impact factor: 3.816

4.  ESTIMATING CAUSAL EFFECTS IN STUDIES OF HUMAN BRAIN FUNCTION: NEW MODELS, METHODS AND ESTIMANDS.

Authors:  Michael E Sobel; Martin A Lindquist
Journal:  Ann Appl Stat       Date:  2020-04-16       Impact factor: 2.083

5.  Causal Inference for fMRI Time Series Data with Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat.

Authors:  Michael E Sobel; Martin A Lindquist
Journal:  J Am Stat Assoc       Date:  2014-07       Impact factor: 5.033

6.  Big Data and Neuroimaging.

Authors:  Yenny Webb-Vargas; Shaojie Chen; Aaron Fisher; Amanda Mejia; Yuting Xu; Ciprian Crainiceanu; Brian Caffo; Martin A Lindquist
Journal:  Stat Biosci       Date:  2017-05-22

7.  Inference with interference between units in an fMRI experiment of motor inhibition.

Authors:  Xi Luo; Dylan S Small; Chiang-Shan R Li; Paul R Rosenbaum
Journal:  J Am Stat Assoc       Date:  2012       Impact factor: 5.033

8.  Functional Causal Mediation Analysis With an Application to Brain Connectivity.

Authors:  Martin A Lindquist
Journal:  J Am Stat Assoc       Date:  2012-12-21       Impact factor: 5.033

9.  Heterogeneous Graphical Granger Causality by Minimum Message Length.

Authors:  Kateřina Hlaváčková-Schindler; Claudia Plant
Journal:  Entropy (Basel)       Date:  2020-12-11       Impact factor: 2.524

  9 in total

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