Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms

2019 ◽  
Vol 31 (11) ◽  
pp. 2035-2050 ◽  
Author(s):  
Lu Zhang ◽  
Yongkai Wu ◽  
Xintao Wu
Keyword(s):  
Causal Modeling ◽  
Discrimination Discovery

scholarly journals Regression dynamic causal modeling for resting‐state fMRI

2021 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Dynamic Causal Modeling

scholarly journals Optimizing Consistency and Coverage in Configurational Causal Modeling

2021 ◽  
pp. 004912412199555
Author(s):  
Michael Baumgartner ◽  
Mathias Ambühl
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Comparative Methods ◽  
Causal Models ◽  
Causal Modeling ◽  
Model Fit

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

A rigorous approach for testing the constructionist hypotheses of brain function

2012 ◽  
Vol 35 (3) ◽  
pp. 148-149 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
K. Sathian ◽  
Xiaoping Hu ◽  
Joseph A. Buckhalt
Keyword(s):  
Granger Causality ◽  
Strong Evidence ◽  
Bayesian Modeling ◽  
Brain Function ◽  
Brain Regions ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Rigorous Approach ◽  
Target Article ◽  
Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

Causal modeling with multivariate species data

2013 ◽  
Vol 448 ◽  
pp. 72-84 ◽  
Author(s):  
Warren L. Paul ◽  
Marti J. Anderson
Keyword(s):  
Causal Modeling

Dynamic causal modeling: A generative model of slice timing in fMRI

NeuroImage ◽  
2007 ◽  
Vol 34 (4) ◽  
pp. 1487-1496 ◽  
Author(s):  
Stefan J. Kiebel ◽  
Stefan Klöppel ◽  
Nikolaus Weiskopf ◽  
Karl J. Friston
Keyword(s):  
Generative Model ◽  
Causal Modeling ◽  
Dynamic Causal Modeling
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