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 392 articles , 1 standard article )
Showing results 1 to 20 of 392.
Sorted by year (- Bühlmann, Peter: Invariance, causality and robustness (2020)
- Córdoba, Irene; Bielza, Concha; Larrañaga, Pedro: A review of Gaussian Markov models for conditional independence (2020)
- Drton, Mathias; Robeva, Elina; Weihs, Luca: Nested covariance determinants and restricted trek separation in Gaussian graphical models (2020)
- Guo, Xiao; Zhang, Hai: Sparse directed acyclic graphs incorporating the covariates (2020)
- Javidian, Mohammad Ali; Valtorta, Marco; Jamshidi, Pooyan: AMP chain graphs: minimal separators and structure learning algorithms (2020)
- Peng, Si; Shen, Xiaotong; Pan, Wei: Reconstruction of a directed acyclic graph with intervention (2020)
- Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
- Rantanen, Kari; Hyttinen, Antti; Järvisalo, Matti: Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach (2020)
- Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Rejoinder on: “Hierarchical inference for genome-wide association studies: a view on methodology with software” (2020)
- Saggioro, Elena; de Wiljes, Jana; Kretschmer, Marlene; Runge, Jakob: Reconstructing regime-dependent causal relationships from observational time series (2020)
- Shah, Rajen D.; Peters, Jonas: The hardness of conditional independence testing and the generalised covariance measure (2020)
- Sinha, S.; Vaidya, U.: On data-driven computation of information transfer for causal inference in discrete-time dynamical systems (2020)
- Vitale, Vincenzina; Musella, Flaminia; Vicard, Paola; Guizzi, Valentina: Modelling an energy market with Bayesian networks for non-normal data (2020)
- Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
- Zeng, Yan; Hao, Zhifeng; Cai, Ruichu; Xie, Feng; Ou, Liang; Huang, Ruihui: A causal discovery algorithm based on the prior selection of leaf nodes (2020)
- Boge, Florian J.: The best of many worlds, or, is quantum decoherence the manifestation of a disposition? (2019)
- Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
- Castelletti, Federico; Consonni, Guido: Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways (2019)
- Costa, Lilia; Smith, James Q.; Nichols, Thomas: A group analysis using the multiregression dynamic models for fMRI networked time series (2019)
- Cui, Ruifei; Groot, Perry; Heskes, Tom: Learning causal structure from mixed data with missing values using Gaussian copula models (2019)
Further publications can be found at: http://www.phil.cmu.edu/tetrad/publications.html