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 441 articles , 1 standard article )
Showing results 1 to 20 of 441.
Sorted by year (- Bazin, Alexandre; Couceiro, Miguel; Devignes, Marie-Dominique; Napoli, Amedeo: Steps towards causal Formal Concept Analysis (2022)
- Fang, Zhuangyan; Liu, Yue; Geng, Zhi; Zhu, Shengyu; He, Yangbo: A local method for identifying causal relations under Markov equivalence (2022)
- Federico Castelletti, Alessandro Mascaro: BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs (2022) arXiv
- Lin, Hanti: Modes of convergence to the truth: steps toward a better epistemology of induction (2022)
- Ma, Dewei; Ren, Weijie; Han, Min: A two-stage causality method for time series prediction based on feature selection and momentary conditional independence (2022)
- Misra, Pratik; Sullivant, Seth: Directed Gaussian graphical models with toric vanishing ideals (2022)
- Rodríguez-López, Verónica; Sucar, Luis Enrique: Knowledge transfer for causal discovery (2022)
- Volvach, A. E.; Kogan, L. P.; Kanonidi, K. H.; Nadezhka, L. I.; Bubukin, I. T.; Shtenberg, V. B.; Gordetsov, A. S.; Krasnikova, O. V.; Kislitsyn, D. I.: Changes in the properties of the statistics of physical and biophysical fields as earthquake precursor (2022)
- Williams, Porter: Entanglement, complexity, and causal asymmetry in quantum theories (2022)
- Baccalá, Luiz A.; Sameshima, Koichi: Partial directed coherence: twenty years on some history and an appraisal (2021)
- Barbero, Fausto; Sandu, Gabriel: Team semantics for interventionist counterfactuals: observations vs. interventions (2021)
- Bongers, Stephan; Forré, Patrick; Peters, Jonas; Mooij, Joris M.: Foundations of structural causal models with cycles and latent variables (2021)
- Buck, Johannes; Klüppelberg, Claudia: Recursive max-linear models with propagating noise (2021)
- Castelletti, Federico; Mascaro, Alessandro: Structural learning and estimation of joint causal effects among network-dependent variables (2021)
- Castelletti, Federico; Peluso, Stefano: Equivalence class selection of categorical graphical models (2021)
- Chiribella, Giulio; Swati: Fast tests for probing the causal structure of quantum processes (2021)
- Constantinou, Anthony C.; Liu, Yang; Chobtham, Kiattikun; Guo, Zhigao; Kitson, Neville K.: Large-scale empirical validation of Bayesian network structure learning algorithms with noisy data (2021)
- Economou, Polychronis; Batsidis, Apostolos; Tzavelas, George; Malefaki, Sonia: Understanding the sampling bias: a case study on NBA drafts (2021)
- Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian: Causal discovery in heavy-tailed models (2021)
- Javidian, Mohammad Ali; Valtorta, Marco: A decomposition-based algorithm for learning the structure of multivariate regression chain graphs (2021)
Further publications can be found at: http://www.phil.cmu.edu/tetrad/publications.html