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. ...


References in zbMATH (referenced in 412 articles , 1 standard article )

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  1. Baccalá, Luiz A.; Sameshima, Koichi: Partial directed coherence: twenty years on some history and an appraisal (2021)
  2. Barbero, Fausto; Sandu, Gabriel: Team semantics for interventionist counterfactuals: observations vs. interventions (2021)
  3. Buck, Johannes; Klüppelberg, Claudia: Recursive max-linear models with propagating noise (2021)
  4. Chiribella, Giulio; Swati: Fast tests for probing the causal structure of quantum processes (2021)
  5. Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian: Causal discovery in heavy-tailed models (2021)
  6. Misra, Pratik; Sullivant, Seth: Gaussian graphical models with toric vanishing ideals (2021)
  7. Park, Gunwoong; Kim, Yesool: Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances (2021)
  8. Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
  9. Robeva, Elina; Seby, Jean-Baptiste: Multi-trek separation in linear structural equation models (2021)
  10. Rossell, David; Zwiernik, Piotr: Dependence in elliptical partial correlation graphs (2021)
  11. Smirnov, Dmitry A.: Phase-dynamic causalities within dynamical effects framework (2021)
  12. Wang, Bingling; Zhou, Qing: Causal network learning with non-invertible functional relationships (2021)
  13. Yang, Jenny; Liu, Yang; Liu, Yufeng; Sun, Wei: Model free estimation of graphical model using gene expression data (2021)
  14. Alrajeh, Dalal; Chockler, Hana; Halpern, Joseph Y.: Combining experts’ causal judgments (2020)
  15. Bühlmann, Peter: Invariance, causality and robustness (2020)
  16. Comin, Cesar H.; Peron, Thomas; Silva, Filipi N.; Amancio, Diego R.; Rodrigues, Francisco A.; Costa, Luciano da F.: Complex systems: features, similarity and connectivity (2020)
  17. Córdoba, Irene; Bielza, Concha; Larrañaga, Pedro: A review of Gaussian Markov models for conditional independence (2020)
  18. Drton, Mathias; Robeva, Elina; Weihs, Luca: Nested covariance determinants and restricted trek separation in Gaussian graphical models (2020)
  19. Evans, Robin J.: Model selection and local geometry (2020)
  20. Guo, Xiao; Zhang, Hai: Sparse directed acyclic graphs incorporating the covariates (2020)

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Further publications can be found at: http://www.phil.cmu.edu/tetrad/publications.html