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            <h2><font color="#ffffff">Inside the Search Box</font></h2>
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<p>The search box in the main workspace area looks like this:</p>
<p><img src="../../images/search_from_data_highlight.gif" width="196" height="67"></p>
<p>if the search is being conducted over a data set, or this:</p>
<p><img src="../../images/search_from_graph_highlight.gif" width="197" height="67"></p>
<p>if the search is being conducted directly over a graph (as a source of true conditional independence facts, to see
    how the algorithm behaves ideally).</p>
<p><font color="#000000">Tetrad has a variety of search algorithms to assist in
    searching for causal explanations of a body of data.</font></p>
<p>It should be noted that the Tetrad search procedures are exponential in the worst case (when all pairs of variables
    are dependent conditional on every other set of variables.) The search procedures may take a good bit of time, and
    there is no guarantee beforehand as to how long that will be.</p>
<p><font color="#000000">&nbsp;These search algorithms are
    different from those conventionally used in statistics.</font></p>
<ol>
    <li><font color="#000000">There are several search algorithms, differing in the assumptions they make.<br>
    </font></li>
    <li><font color="#000000">Many of the search algorithms allow the user to specify background information that will
        be used in the search procedure. In many cases the search results will be uninformative unless such background
        assumptions are explicitly made. This design not only provides for more flexibility, it also encourages the user
        to be conscious of the additional assumptions imposed in deriving a model from data.<br>
    </font></li>
    <li><font color="#000000">Even with background assumptions, data often do not determine a unique best or robust
        explanation. The search algorithms take in data and return information about a <span
                style="font-style: italic;">collection</span> of alternative causal graphs that can explain features of
        the data. They do not usually return a unique graph, although they sometimes will if sufficient prior knowledge
        is specified. In contrast</font><font
            color="#000000">, if one searches for a regression model of the influences of a set of variables on a
        selected variable, a regression model will certainly be found (provided there are sufficient data points),
        specifying which variables influence the target and which do not. <br>
    </font></li>
    <li><font color="#000000"> The algorithms are in some respects cautious. Search algorithms such as FCI and PC ,
        described below, will often say, correctly, that it cannot be determined whether or not a particular variable
        influences another.<br>
    </font></li>
    <li><font color="#000000">The algorithms are not just useful guesses.&nbsp; Under explicit assumptions (which often
        hold at best only approximately), the algorithms are "pointwise consistent"--they converge almost surely to the
        correct answer. The conditions for this sort of consistency of the seaarch procedures are described in the
        references. Conventional model search algorithms--stepwise regression, for example--have such guarantees only
        under very strong prior assumptions about the causal structure.<br>
    </font></li>
    <li><font color="#000000">The output of the search algorithms provides a variety of indirect information about how
        much the conclusions of the algorithm can be trusted. They can, for example, be run repeatedly for a variety of
        specifications of depErrorsAlpha values in their statistical tests, to gain insight about robustness. For search
        algorithms such as PC, CCD, GES and MimBuild, described below,&nbsp; if the search algorithms return
        "spaghetti"--a highly connected graph--that indicates the serach cannot determine whether all of the connected
        variables may be influenced&nbsp; by a common unmeasured cause. If the PC algoirthm returns an edge with two
        arrowheads, that indicates a latent variable may be acting; if searches other than CCD return graphs with
        cycles, that indicates the assumptions of the search algoirthm are violated. <br>
    </font></li>
    <li><font color="#000000">Some of the search procedures are robust against common difficulties in sampling
        designs--they give correct, but reduced information in such cases. For example, the FCI algorithm allows that
        there may be unobserved latent common causes of measured variables--or not--and that the sample may have been
        formed by a process in which the values of measured variables invlucne whether or not a unit is included in the
        sample (<span style="font-style: italic;">sample selection bias</span>). The CCD algorithm allows that the
        correct causal structure may be "non-recursive"--essentially a cyclic graphical mode, a folded up time
        series.<br>
    </font></li>
    <li><font color="#000000">The output of the algorithms is not an estiamted model with parameter values, but a
        discription of a class of causall graphs that can explain statistical features of the data considered by the
        search procedures. That information can be converted by hand into particular graphical models in the form of
        directed graphs, which can then be estimated by the program and tested.</font></li>
</ol>
<p><font color="#000000">The search procedures available are named:</font></p>
<ul>
    <li><font color="#000000"><a href="pc.html">PC</a> - Searches for </font><font color="#000000"> Bayes net or SEM
        models when it is assumed there is no latent (unrecorded) variable that
        contributes to the association of two or more measured variables.</font></li>
    <li><font color="#000000">CPC - Variant of PC that improves arrow orientation accuracy. </font></li>
    <li>PCD - Variant of PC that can be applied to deterministic data.</li>
    <li><font color="#000000"><a href="fci.html">FCI </a>--which performs a search similar to PC but allowing that there
        may be latent variables.</font></li>
    <li><font color="#000000"><a href="ccd.html">CCD</a>--for searching for non-recursive SEM models (models of feedback
        systems using cyclic graphs) without latent variables </font></li>
    <li><a href="ges.html">GES</a> -- <font color="#000000">Scoring search for Bayes net or SEM models when it is
        assumed there is no latent (unrecorded) variable that contributes to the association of two or more measured
        variables.</font></li>
    <li><font color="#000000"><a href="mbf.html">MBF</a> -- Searches for the Markov blankets DAGs for a given target T
        over a list of variables &lt;v1,...,vn,T&gt;.</font><font color="#000000"></font></li>
    <li>CEF - Variant of MBF that searches for the causal environment of a T (i.e., parents and children of T).</li>
    <li>Structural EM -</li>
    <li><font color="#000000"><a href="mimbuild.html">MimBuild</a>--for searching for latent structure from the output
        of Build Pure Clusters or Purify Clusters</font></li>
    <li><font color="#000000"><a href="bpc.html">BPC</a> --for searching for sets of variables that share a single
        latent common cause </font></li>
    <li><font color="#000000"><a href="bpc.html">Purify Clusters</a>--</font><font color="#000000">for searching for
        sets of variables that share a single latent common cause</font><font color="#000000"><br>
    </font></li>
</ul>
<h3>Inputs to the Search Box
</h3>
<p><font color="#000000">There are two possible inputs for a search
    algorithm: a data set or a graph. If a graph is input, the program
    allows searches the program computes implied independence and
    conditional independence relations and allows you to conduct any search
    that uses only such constraints--the PC, FCI and CCD algorithms.&nbsp; </font></p>
<p><font color="#000000">Why would you apply a Search procedure to a
    model you already know?&nbsp; For a very important reason: The Search
    procedures will find the graphical representation of alternative
    models&nbsp; to your model that imply the same constraints. </font></p>
The more usual use of the search algorithms requires a data set as
input. Here is an example.
<ul>
    <li><font color="#000000">Select the Search button.</font>
        <p><img src="../../images/search_button.gif" width="114" height="45"></p>
    </li>
    <li><font color="#000000">Click in the workbench to create a Search
        icon.</font></li>
    <li><font color="#000000">Use the Flow Charter button to connect the
        Data icon to the Search icon.</font>
        <p><img src="../../images/arrow_button.gif" width="114" height="44"></p>
    </li>
    <li><font color="#000000">Double-click the Search icon to choose an
        search procedure.</font>
        <p><img src="../../images/searchCsreation.png" width="512" height="248"></p>
    </li>
</ul>
<p><font color="#000000"><b><a name="SelectingSearch"
                               id="SelectingSearch"></a><br>
    Selecting a Search procedure</b> </font></p>
<p><font color="#000000">Tetrad offers the following choices of search
    algorithms. For more details about the assumptions and parameters
    needed for each algorithm, click in the respective links. </font></p>
<p><font color="#000000">There are two main classes of algorithms. The
    first one is designed for general graphs with or without assuming the
    possibility of hidden common causes:</font></p>
<ul>
    <li><font color="#000000"><b><a href="pc.html">PC algorithm</a></b>:
        this method assumes that there are no hidden common causes between observed
        variables in the input (i.e., variables from the data set, or observed variables
        in the input graph) and that the graphical structure sought has no cycles.</font></li>
</ul>
<ul>
    <li><font color="#000000"><b><a href="fci.html">FCI algorithm</a></b>:
        this method does not assume that there are no hidden common causes between
        observed variables in the input (i.e., variables from the data set, or observed
        variables in the input graph); it does assume that the graphical strucutre
        sought has no cycles.<br>
    </font></li>
    <li><font color="#000000"><b><a href="ccd.html">CCD algorithm</a></b>:
        this method assumes there are no hidden common causes; it allows cycles; it
        is only correct for discrete variables under a restrictive assumtptions<br>
    </font></li>
    <li><font color="#000000"><b><a href="ges.html">GES algorithm</a></b>:
        same assumptions as the PC algorithm, except that this one performs search
        by scoring a graph by its asymptotic posterior probability.</font></li>
</ul>
<p><font color="#000000">The second class concerns algorithms to search
    for&nbsp; latent variable structural equation models from data and
    background knowledge.<br>
</font></p>
<ul>
    <li><font color="#000000"><b><a href="mimbuild.html">MIM Build algorithm</a></b>:
        learns the causal relationships among latent variables, when the true (unknown)
        data generation process is believed to be a pure measurement/structural model.</font></li>
</ul>
<ul>
    <li><font color="#000000"><b><a href="bpc.html">Build Pure Clusters
        algorithm</a></b>: a complement to MIM Build and Purify, this algorithm learns
        the causal relationships from latent variables to observed variables, when
        the true (unknown) data generation process is believed to be contain a pure
        measurement/structural submodel--i.e. a model in which each <br>
    </font></li>
    <li><font color="#000000"><b><a href="purify.html">Purify algorithm</a></b>:
        given a measurement model, this method searches for a submodel in which there
        are no every measured variable is influenced by one and only one latent variable.<br>
    </font></li>
</ul>
<p>Select the desired algorithm that meets your assumptions from the
    Search list. An initial dialog box showing the search parameters you
    can set is displayed. The following figure illustrates the one that is
    displayed when PC Algorithm is selected.</p>
<p><font color="#000000"><img src="../../images/pcsearch_dialog.png"
                              width="268" height="209"></font></p>
<p>After the proper parameters are set, if the user checks the box
    "Execute searches automatically", the automated search procedure will
    start when the OK button is clicked. The respective Help button can be
    used to get instructions about that specific algorithm. The next window
    displays the result of the procedure, and can also be used to fire new
    searches. The following figure illustrates an output for the PC
    algorithm.</p>
<p><font color="#000000"><img src="../../images/pc_output.png" width="588"
                              height="519"></font></p>
<p><font color="#000000"><b><br>
    Inserting background knowledge</b></font></p>
<p><font color="#000000">Besides the assumptions underlying each
    algorithm, another source of constraints that can be used by the search
    procedures to narrow down the search and return a more informative
    output is making use of background knowledge provided by the user. To see how to specify background knowledge for a
    search algorithm, see <a href="../../common_tasks/editing_knowledge.html">Editing Knowledge</a>.</font></p>
<p><span style="font-weight: bold;">Assumptions<br>
  <br>
</span>A search procedure is pointwise consistent if as the sample size
    increases without bound, the output of the algorithm converges with
    probability 1 to<br>
    true information about the data generating structure. For all of the
    Tetrad search algorithms, available proofs of pointwise consistency,
    assume at least the following:<br>
    <br>
    1. The sample is i.i.d--the probaiblity of any unit in a population
    being sampled is the same as any other, and the joint probability
    distribution of the variables is the same for all units.<br>
    <br>
    2. The joint probability distribution is locally Markov. In acyclic
    cases, this is equivalent to a simpler "global" Markov condition:&nbsp;
    that a variable is independent of all variables that are not its
    effects conditional on the direct causes of the variable in the causal
    graph (its "parents"). In cyclic cases, the local Markov condition has
    a related but more technical definition. (See Spirtes, et al., 2000).<br>
    <br>
    3. All of the independence and conditional independence relations in
    the joint probability distribution are consequences of the local Markov
    condition for the true causal graph.<br>
    <br>
    In addition, various specific search algorithms impose other assumptions. Of course,
    the search algorithms<span
            style="font-style: italic;"> may</span> give&nbsp; correct in information when
    these assumptions do not strictly hold, and in some cases will do so when they
    are grossly violated--the PC algoirthm, for example, will sometimes correctly
    identify the presence of unrecorded common causes of recorded variables.</p>
<p>Types of Searches:</p>
<ul>
    <li><a href="pc.html">PC</a></li>
    <li><a href="cpc.html">CPC</a></li>
    <li><a href="pcd.html">PCD</a></li>
    <li><a href="fci.html">FCI</a></li>
    <li><a href="cfci.html">CFCI</a></li>
    <li><a href="ccd.html">CCD</a></li>
    <li><a href="ges.html">GES</a></li>
    <li><a href="mbf.html">MBF</a></li>
    <li><a href="mimbuild.html">MIMBuild</a></li>
    <li><a href="bpc.html">BPC</a></li>
    <li><a href="purify.html">Purity</a><br>
    </li>
</ul>
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