TETRAD
TETRAD is a program which creates, simulates data from, estimates, tests, predicts with, and searches for causal and statistical models. The aim of the program is to provide sophisticated methods in a friendly interface requiring very little statistical sophistication of the user and no programming knowledge. It is not intended to replace flexible statistical programming systems such as Matlab, Splus or R. Tetrad is freeware that performs many of the functions in commercial programs such as Netica, Hugin, LISREL, EQS and other programs, and many discovery functions these commercial programs do not perform. Tetrad is unique in the suite of principled search (”exploration,” ”discovery”) algorithms it provides--for example its ability to search when there may be unobserved confounders of measured variables, to search for models of latent structure, and to search for linear feedback models--and in the ability to calculate predictions of the effects of interventions or experiments based on a model. All of its search procedures are ”pointwise consistent”--they are guaranteed to converge almost certainly to correct information about the true structure in the large sample limit, provided that structure and the sample data satisfy various commonly made (but not always true!) assumptions. ...
Keywords for this software
References in zbMATH (referenced in 265 articles , 1 standard article )
Showing results 1 to 20 of 265.
Sorted by year (- Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)
- Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
- Haws, David; Cussens, James; Studený, Milan: Polyhedral approaches to learning Bayesian networks (2017)
- Hyttinen, Antti; Plis, Sergey; Järvisalo, Matti; Eberhardt, Frederick; Danks, David: A constraint optimization approach to causal discovery from subsampled time series data (2017)
- Li, Benchong; Li, Yang: A note on faithfulness and total positivity (2017)
- Malinsky, Daniel; Spirtes, Peter: Estimating bounds on causal effects in high-dimensional and possibly confounded systems (2017)
- Nie, Siqi; de Campos, Cassio P.; Ji, Qiang: Efficient learning of Bayesian networks with bounded tree-width (2017)
- Parida, Pramod Kumar; Marwala, Tshilidzi; Chakraverty, Snehashish: An overview of recent advancements in causal studies (2017)
- Parviainen, Pekka; Kaski, Samuel: Learning structures of Bayesian networks for variable groups (2017)
- Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
- Xu, Jing; Tong, Xing-wei; Wang, Fang; Chen, Jian-ping: Learning dynamic causal relationships among sugar prices (2017)
- Aghdam, Rosa; Alijanpour, Mohsen; Azadi, Mehrdad; Ebrahimi, Ali; Eslahchi, Changiz; Rezvan, Abolfazl: Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method (2016)
- Alyami, Salem A.; Azad, A.K.M.; Keith, Jonathan M.: Uniform sampling of directed and undirected graphs conditional on vertex connectivity (2016)
- Balabanov, O.S.: Induced dependence, factor interaction, and discriminating between causal structures (2016)
- Chu, Victor W.; Wong, Raymond K.; Chen, Fang; Fong, Simon; Hung, Patrick C.K.: Self-regularized causal structure discovery for trajectory-based networks (2016)
- Drton, Mathias; Xiao, Han: Wald tests of singular hypotheses (2016)
- Eberhardt, Frederick: Green and grue causal variables (2016)
- Glymour, Clark: Clark Glymour’s responses to the contributions to the Synthese special issue “Causation, probability, and truth: the philosophy of Clark Glymour” (2016)
- Goudie, Robert J.B.; Mukherjee, Sach: A Gibbs sampler for learning DAGs (2016)
- Hagmayer, York: Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research (2016)
Further publications can be found at: http://www.phil.cmu.edu/tetrad/publications.html