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    Introduction to TETRAD
    



    
    
        
            Tetrad Overview
        
    
    

What is Tetrad? 
Tetrad is a program for 

    creating,
    simulating data from,
    estimating,
    testing,
    predicting with,
    and searching for

causal/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.
Tetrad is limited to models of categorical data (which can also be used for
    ordinal data) and to linear models ("structural equation models') with a Normal
    probability distribution, and to a very
    limited class of time series models. The Tetrad programs describe causal models
    in three distinct parts or stages: a picture, representing a directed graph
    specifying hypothetical causal relations among the variables; a specification
    of the family of probability distributions and kinds of parameters associated
    with the graphical model; and a specification of the numerical values of those
    parameters.
The program and its search algorithms have been developed over several years
    with support from the National Aeronautics and Space Administration and the
    Office of Naval Research. Joseph Ramsey has implemented most of the program,
    with substantial

    assistance from Frank Wimberly. Executable and Source code for all versions
    of Tetrad IV, and this manual, are copyrighted, 2004, by Clark Glymour, Richard
    Scheines, Peter Spirtes and Joseph Ramsey. The program may be freely downloaded
    and used without permission of copyright holders, who reserve the right to alter
    the program at any time without notification.
The Tetrad suite of programs permits the user to do any of the following:

    Generate a graphical statistical/causal 
        model of any of the following kinds:
    
    
        Models for categorical data (Bayes networks);
        Models for continuous data with variables having a Gaussian (Normal) joint
            probability distribution;
        
        Models for a limited class of time-series representing genetic regulatory
            networks..
        
    
    Estimate parameters of models of
        the following kinds:
    
    
        Models for categorical data in which all variables are recorded in the
            data (no "latent" variables);
        
        Models for continuous data with or without latent variables;
    
    Test the fit of models of any of
        the kinds listed in 2. above.
    
    Simulate data from a model. or
        any of the kinds listed in 1. above.
    
    Update models of categorical data;
        i.e.,, compute the probability of any variable in the model conditional on
        any set of values for other variables in the model.
    
    Predict the probability of a variable
        in a model (without latent variables) from interventions that fix or randomize
        values for any set of other variables in the model.
    
    Search for models:
    
        Of categorical data with or without latent variables;
        Of continuous, Gaussian data with or without latent variables.
    
    Compare graphical features of two
        models.
    
    Find alternative models statistically
        equivalent to any given model without latent variables.
    
    Select variables within a dataset
        for classifying values of cases of another variable in the dataset
    
    Classify new (or old) cases using
        the variables selected in 9. above.
    
    Assess the accuracy of classification.

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