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 215 articles , 1 standard article )

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  1. 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)
  2. Balabanov, O.S.: Induced dependence, factor interaction, and discriminating between causal structures (2016)
  3. Chu, Victor W.; Wong, Raymond K.; Chen, Fang; Fong, Simon; Hung, Patrick C.K.: Self-regularized causal structure discovery for trajectory-based networks (2016)
  4. Goudie, Robert J.B.; Mukherjee, Sach: A Gibbs sampler for learning DAGs (2016)
  5. Karimnezhad, Ali; Moradi, Fahimeh: Bayesian parameter learning with an application (2016)
  6. Oates, Chris.J.; Smith, Jim Q.; Mukherjee, Sach: Estimating causal structure using conditional DAG models (2016)
  7. Peña, Jose M.; Gómez-Olmedo, Manuel: Learning marginal AMP chain graphs under faithfulness revisited (2016)
  8. Silva, Ricardo; Evans, Robin: Causal inference through a witness protection program (2016)
  9. Xu, Jing; Tong, Xing-wei: Causality analysis of futures sugar prices in Zhengzhou based on graphical models for multivariate time series (2016)
  10. Yang, Cuicui; Ji, Junzhong; Liu, Jiming; Liu, Jinduo; Yin, Baocai: Structural learning of Bayesian networks by bacterial foraging optimization (2016)
  11. Bontempi, Gianluca; Flauder, Maxime: From dependency to causality: a machine learning approach (2015)
  12. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Reducing the structure space of Bayesian classifiers using some general algorithms (2015)
  13. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks (2015)
  14. Briggs, Rachael: Foundations of probability (2015)
  15. Fox, Christopher J.; Käufl, Andreas; Drton, Mathias: On the causal interpretation of acyclic mixed graphs under multivariate normality (2015)
  16. He, Yangbo; Jia, Jinzhu; Yu, Bin: Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs (2015)
  17. Hofer-Szabó, Gábor: Relating Bell’s local causality to the causal Markov condition (2015)
  18. Huber, Franz: What should I believe about what would have been the case? (2015)
  19. Janzing, Dominik; Steudel, Bastian; Shajarisales, Naji; Schölkopf, Bernhard: Justifying information-geometric causal inference (2015)
  20. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)

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