Literature DB >> 22119004

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

Martin A Lindquist1, Michael E Sobel.   

Abstract

Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes' notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour's comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 22119004      PMCID: PMC4121662          DOI: 10.1016/j.neuroimage.2011.11.027

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


  8 in total

1.  Principal stratification in causal inference.

Authors:  Constantine E Frangakis; Donald B Rubin
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

2.  Identifiability and exchangeability for direct and indirect effects.

Authors:  J M Robins; S Greenland
Journal:  Epidemiology       Date:  1992-03       Impact factor: 4.822

3.  Validity and power in hemodynamic response modeling: a comparison study and a new approach.

Authors:  Martin A Lindquist; Tor D Wager
Journal:  Hum Brain Mapp       Date:  2007-08       Impact factor: 5.038

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

Authors:  Martin A Lindquist; Michael E Sobel
Journal:  Neuroimage       Date:  2010-10-21       Impact factor: 6.556

5.  Counterfactuals, graphical causal models and potential outcomes: response to Lindquist and Sobel.

Authors:  Clark Glymour
Journal:  Neuroimage       Date:  2011-07-30       Impact factor: 6.556

6.  Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling.

Authors:  Martin A Lindquist; Ji Meng Loh; Lauren Y Atlas; Tor D Wager
Journal:  Neuroimage       Date:  2008-11-21       Impact factor: 6.556

7.  Brain mediators of cardiovascular responses to social threat: part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity.

Authors:  Tor D Wager; Christian E Waugh; Martin Lindquist; Doug C Noll; Barbara L Fredrickson; Stephan F Taylor
Journal:  Neuroimage       Date:  2009-05-22       Impact factor: 6.556

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

  8 in total
  6 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.  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

3.  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

4.  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

5.  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

6.  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

  6 in total

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