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 278 articles , 1 standard article )

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  1. Beirlaen, Mathieu; Leuridan, Bert; Van De Putte, Frederik: A logic for the discovery of deterministic causal regularities (2018)
  2. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using $\ell_1$-penalized likelihood (2018)
  3. Gissibl, Nadine; Klüppelberg, Claudia: Max-linear models on directed acyclic graphs (2018)
  4. Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)
  5. Sechidis, Konstantinos; Brown, Gavin: Simple strategies for semi-supervised feature selection (2018)
  6. Costa, Lilia; Nichols, Thomas; Smith, Jim Q.: Studying the effective brain connectivity using multiregression dynamic models (2017)
  7. Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
  8. Fisher, Tyrus: Counterlegal dependence and causation’s arrows: causal models for backtrackers and counterlegals (2017)
  9. Haws, David; Cussens, James; Studený, Milan: Polyhedral approaches to learning Bayesian networks (2017)
  10. Hyttinen, Antti; Plis, Sergey; Järvisalo, Matti; Eberhardt, Frederick; Danks, David: A constraint optimization approach to causal discovery from subsampled time series data (2017)
  11. Li, Benchong; Li, Yang: A note on faithfulness and total positivity (2017)
  12. Malinsky, Daniel; Spirtes, Peter: Estimating bounds on causal effects in high-dimensional and possibly confounded systems (2017)
  13. Nie, Siqi; de Campos, Cassio P.; Ji, Qiang: Efficient learning of Bayesian networks with bounded tree-width (2017)
  14. Parida, Pramod Kumar; Marwala, Tshilidzi; Chakraverty, Snehashish: An overview of recent advancements in causal studies (2017)
  15. Parviainen, Pekka; Kaski, Samuel: Learning structures of Bayesian networks for variable groups (2017)
  16. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  17. Stern, Reuben: Interventionist decision theory (2017)
  18. Xu, Jing; Tong, Xing-wei; Wang, Fang; Chen, Jian-ping: Learning dynamic causal relationships among sugar prices (2017)
  19. 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)
  20. Alyami, Salem A.; Azad, A.K.M.; Keith, Jonathan M.: Uniform sampling of directed and undirected graphs conditional on vertex connectivity (2016)

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