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 305 articles , 1 standard article )

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  1. Costa, Lilia; Smith, James Q.; Nichols, Thomas: A group analysis using the multiregression dynamic models for fMRI networked time series (2019)
  2. Beirlaen, Mathieu; Leuridan, Bert; Van De Putte, Frederik: A logic for the discovery of deterministic causal regularities (2018)
  3. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using $\ell_1$-penalized likelihood (2018)
  4. Chen, Chen; Ren, Min; Zhang, Min; Zhang, Dabao: A two-stage penalized least squares method for constructing large systems of structural equations (2018)
  5. Evans, Robin J.: Margins of discrete Bayesian networks (2018)
  6. Gissibl, Nadine; Klüppelberg, Claudia: Max-linear models on directed acyclic graphs (2018)
  7. Landes, Jürgen; Osimani, Barbara; Poellinger, Roland: Epistemology of causal inference in pharmacology, Towards a framework for the assessment of harms (2018)
  8. Lauritzen, Steffen; Sadeghi, Kayvan: Unifying Markov properties for graphical models (2018)
  9. Malinsky, Daniel: Intervening on structure (2018)
  10. Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H.: High-dimensional consistency in score-based and hybrid structure learning (2018)
  11. Park, Gunwoong; Raskutti, Garvesh: Learning quadratic variance function (QVF) DAG models via overdispersion scoring (ODS) (2018)
  12. Perković, Emilija; Textor, Johannes; Kalisch, Markus; Maathuis, Marloes H.: Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs (2018)
  13. Prestwich, S. D.; Tarim, S. A.; Ozkan, I.: A new causal discovery heuristic (2018)
  14. Radhakrishnan, Adityanarayanan; Solus, Liam; Uhler, Caroline: Counting Markov equivalence classes for DAG models on trees (2018)
  15. Rothenhäusler, Dominik; Ernest, Jan; Bühlmann, Peter: Causal inference in partially linear structural equation models (2018)
  16. Runge, J.: Causal network reconstruction from time series: from theoretical assumptions to practical estimation (2018)
  17. Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)
  18. Sechidis, Konstantinos; Brown, Gavin: Simple strategies for semi-supervised feature selection (2018)
  19. Swanson, Sonja A.; Hernán, Miguel A.; Miller, Matthew; Robins, James M.; Richardson, Thomas S.: Partial identification of the average treatment effect using instrumental variables: review of methods for binary instruments, treatments, and outcomes (2018)
  20. Weinberger, Naftali: Faithfulness, coordination and causal coincidences (2018)

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