Causal Inference using Graphical Models with the R Package pcalg
Reads0
Chats0
TLDR
The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data.Abstract:
The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this document, we give a brief overview of the methodology, and demonstrate the package’s functionality in both toy examples and applications.read more
Citations
More filters
Journal ArticleDOI
Qgraph: Network visualizations of relationships in psychometric data
Sacha Epskamp,Angélique O. J. Cramer,Lourens J. Waldorp,Verena D. Schmittmann,Denny Borsboom +4 more
TL;DR: The qgraph package for R is presented, which provides an interface to visualize data through network modeling techniques, and is introduced by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
Journal ArticleDOI
Network Analysis: An Integrative Approach to the Structure of Psychopathology
TL;DR: An examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network).
Posted Content
Estimating Psychological Networks and their Accuracy: A Tutorial Paper
TL;DR: The current state-of-the-art of network estimation is introduced and a rationale why researchers should investigate the accuracy of psychological networks is provided, and the free R-package bootnet is developed that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods.
Journal ArticleDOI
A new method for constructing networks from binary data
Claudia D. van Borkulo,Denny Borsboom,Sacha Epskamp,Tessa F. Blanken,Lynn Boschloo,Robert A. Schoevers,Lourens J. Waldorp +6 more
TL;DR: A method for assessing network structures from binary data based on Ising models, which combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network is presented.
Journal ArticleDOI
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.
TL;DR: The Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) is discussed and its utility as an exploratory data analysis tool is described: which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables.
References
More filters
Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Book
Causation, prediction, and search
TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.
Journal ArticleDOI
Functional Discovery via a Compendium of Expression Profiles
Timothy P. Hughes,Matthew J. Marton,Allan R. Jones,Christopher J. Roberts,Roland Stoughton,Christopher D. Armour,Holly A. Bennett,Ernest M. Coffey,Hongyue Dai,Yudong D. He,Matthew J. Kidd,Amy M King,Michael R. Meyer,David J. Slade,Pek Yee Lum,Sergey B. Stepaniants,Daniel D. Shoemaker,Daniel J Gachotte,Kalpana Chakraburtty,Julian A. Simon,Martin Bard,Stephen H. Friend +21 more
TL;DR: A reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae is constructed, and it is shown that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles.
Journal ArticleDOI
Learning Bayesian Networks with the bnlearn R Package
TL;DR: Thebnlearn as discussed by the authors is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing.
Proceedings Article
Equivalence and synthesis of causal models
Thomas Verma,Judea Pearl +1 more
TL;DR: The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information, which leads to a model theoretic definition of causation in terms of statistical dependencies.