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There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just… (More)

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and… (More)

Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call " Adjacency-Faithfulness " and " Orientation-Faithfulness. " We point out that assuming… (More)

- Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, Richard Scheines +3 others
- Synthese
- 2010

We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples ; (2) cases with larger numbers of causes generate novel puzzles; (3) " neuron " and causal Bayes net… (More)

We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient , continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery.… (More)

Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches… (More)

Data sets with many discrete variables and relatively few cases arise in many domains. Several studies have sought to identify the Markov Blanket (MB) of a target variable by filtering variables using statistical decisions for conditional independence and then applying a classifier using the MB predictors. Other studies have applied the PC algorithm or… (More)

- Zvia Burgansky-Eliash, Gadi Wollstein, Tianjiao Chu, Joseph D Ramsey, Clark Glymour, Robert J Noecker +2 others
- Investigative ophthalmology & visual science
- 2005

PURPOSE
Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection.
METHODS
Forty-seven patients with glaucoma (47… (More)

We consider several alternative ways of exploiting non-Gaussian distributional features, including some that can in principle identify direct, positive feedback relations (graphically, 2-cycles) and combinations of methods that can identify high dimensional graphs. All of the procedures are implemented in the TETRAD freeware (Ramsey et al., 2013). We show… (More)

- Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda +1 other
- UAI
- 2008

An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on… (More)