Package edu.cmu.tetrad.search

Class MarkovCheck

java.lang.Object
edu.cmu.tetrad.search.MarkovCheck
public class MarkovCheck extends Object
Checks whether a graph is Markov given a data set. First, a list of m-separation predictions are made for each pair of variables in the graph given the parents of one of the variables. One list (for Markov) is for where the m-separation holds and another list (for dependency checks) where the m-separation does not hold. Then the predictions are tested against the data set using the independence test. For the Markov test, since an independence test yielding p-values should be Uniform under the null hypothesis, these p-values are tested for Uniformity using the Kolmogorov-Smirnov test. Also, a fraction of dependent judgments is returned, which should equal the alpha level of the independence test if the test is Uniform under the null hypothesis. For the Faithfulness test, the p-values are tested for Uniformity using the Kolmogorov-Smirnov test; these should be dependent. Also, a fraction of dependent judgments is returned.

Knowledge may be supplied to the Markov check. This knowledge is used to specify independence and conditioning ranges. For facts of the form X _||_ Y | Z, X and Y should be in the last tier of the knowledge, and Z should be in previous tiers. Additional forbidden or required edges are not allowed.

Version:
$Id: $Id
Author:
josephramsey

  • Constructor Details

    • MarkovCheck

      public MarkovCheck(Graph graph, IndependenceTest independenceTest, ConditioningSetType setType)
      Constructor. Takes a graph and an independence test over the variables of the graph.
      Parameters:
      graph - The graph.
      independenceTest - The test over the variables of the graph.
      setType - The type of conditioning sets to use in the Markov check.

  • Method Details

    • getAllSubsetsIndependenceFacts

      @NotNull public @NotNull MarkovCheck.AllSubsetsIndependenceFacts getAllSubsetsIndependenceFacts()
      Returns the set of independence facts used in the Markov check, for dseparation and dconnection separately.
      Returns:
      The set of independence facts used in the Markov check, for dseparation and dconnection separately.

    • getMinBeta

      public double getMinBeta(Node x, Node y)
      Calculates the minimum IDA beta value for a given causal relationship between two nodes.
      Parameters:
      x - the cause node
      y - the effect node
      Returns:
      the minimum IDA beta value
      Throws:
      IllegalArgumentException - if the graph is not a CPDAG (partially directed acyclic graph)

    • getVariables

      public List<Node> getVariables(List<Node> graphNodes, List<Node> independenceNodes, List<Node> conditioningNodes)
      Returns the variables of the independence test.
      Parameters:
      graphNodes - a List object
      independenceNodes - a List object
      conditioningNodes - a List object
      Returns:
      The variables of the independence test.

    • generateResults

      public void generateResults()
      Generates all results, for both the Markov and dependency checks, for each node in the graph given the parents of that node. These results are stored in the resultsIndep and resultsDep lists. This should be called before any of the result methods. Note that only results for X _||_ Y | Z1,...,Zn are generated, where X and Y are in the independenceNodes list and Z1,...,Zn are in the conditioningNodes list.
      See Also:

    • getSetType

      public ConditioningSetType getSetType()
      Returns type of conditioning sets to use in the Markov check.
      Returns:
      The type of conditioning sets to use in the Markov check.
      See Also:

    • setSetType

      public void setSetType(ConditioningSetType setType)
      Sets the type of conditioning sets to use in the Markov check.
      Parameters:
      setType - The type of conditioning sets to use in the Markov check.
      See Also:

    • setParallelized

      public void setParallelized(boolean parallelized)
      True if the checks should be parallelized. (Not always a good idea.)
      Parameters:
      parallelized - True if the checks should be parallelized.

    • getResults

      public List<IndependenceResult> getResults(boolean indep)
      After the generateResults method has been called, this method returns the results for the Markov or dependency check, depending on the value of the indep parameter.
      Parameters:
      indep - True for the Markov results, false for the dependency results.
      Returns:
      The results for the Markov or dependency check.

    • getPValues

      public List<Double> getPValues(List<IndependenceResult> results)
      Returns the list of p-values for the given list of results.
      Parameters:
      results - The results.
      Returns:
      Their p-values.

    • getFractionDependent

      public double getFractionDependent(boolean indep)
      Returns the fraction of dependent judgments for the given list of results.
      Parameters:
      indep - True for the Markov results, false for the dependency results.
      Returns:
      The fraction of dependent judgments for this condition.

    • getKsPValue

      public double getKsPValue(boolean indep)
      Returns the Kolmorogov-Smirnov p-value for the given list of results.
      Parameters:
      indep - True for the Markov results, false for the dependency results.
      Returns:
      The Kolmorogov-Smirnov p-value for this condition.

    • getAndersonDarlingA2

      public double getAndersonDarlingA2(boolean indep)
      Returns the Anderson-Darling A^2 statistic for the given list of results.
      Parameters:
      indep - True if for implied independencies, false if for implied dependencies.
      Returns:
      The Anderson-Darling A^2 statistic for the given list of results.

    • getAndersonDarlingA2Star

      public double getAndersonDarlingA2Star(boolean indep)
      Returns the Anderson-Darling A^2* statistic for the given list of results.
      Parameters:
      indep - True if for implied independencies, false if for implied dependencies.
      Returns:
      The Anderson-Darling A^2* statistic for the given list of results.

    • getAndersonDarlingP

      public double getAndersonDarlingP(boolean indep)
      Returns the Anderson-Darling p-value for the given list of results.
      Parameters:
      indep - True if for implied independencies, false if for implied dependencies.
      Returns:
      The Anderson-Darling p-value for the given list of results.

    • getBinomialP

      public double getBinomialP(boolean indep)
      Returns the Binomial p-value for the given list of results.
      Parameters:
      indep - True if for implied independencies, false if for implied dependencies.
      Returns:
      The Binomial p-value for the given list of results.

    • getNumTests

      public int getNumTests(boolean indep)
      Returns the number of tests for the given list of results.
      Parameters:
      indep - True if for implied independencies, false if for implied dependencies.
      Returns:
      The number of tests for the given list of results.

    • getVariable

      public Node getVariable(String name)
      Returns the variable with the given name.
      Parameters:
      name - The name of the variables.
      Returns:
      The variable with the given name.

    • getIndependenceTest

      public IndependenceTest getIndependenceTest()
      Returns the independence test being used.
      Returns:
      This test.

    • setPercentResample

      public void setPercentResample(double percentResample)
      Sets the percentage of all samples to use when resampling for each conditional independence test.
      Parameters:
      percentResample - The percentage of all samples to use when resampling for each conditional independence test.

    • getKnowledge

      public Knowledge getKnowledge()
      Returns the knowledge object for the Markov checker. This knowledge object should contain the tier knowledge for the Markov checker. The last tier contains the possible X and Y for X _||_ Y | Z1,...,Zn, and the previous tiers contain the possible Z1,...,Zn for X _||_ Y | Z1,...,Zn. Additional forbidden or required edges are ignored.
      Returns:
      The knowledge object.

    • setKnowledge

      public void setKnowledge(Knowledge knowledge)
      Sets the knowledge object for the Markov checker. The knowledge object should contain the tier knowledge for the Markov checker. The last tier contains the possible X and Y for X _||_ Y | Z1,...,Zn, and the previous tiers contain the possible Z1,...,Zn for X _||_ Y | Z1,...,Zn. Additional forbidden or required edges are ignored.
      Parameters:
      knowledge - The knowledge object.

    • getMarkovCheckRecord

      public MarkovCheck.MarkovCheckRecord getMarkovCheckRecord()
      Generates the results for the given set of independence facts as a single record.
      Returns:
      The Markov check record.
      See Also:

    • getMarkovCheckRecordString

      public String getMarkovCheckRecordString()
      Returns the Markov check record as a string.
      Returns:
      The Markov check record as a string.
      See Also:

    • getIndependenceNodes

      public List<Node> getIndependenceNodes()
      Returns the nodes that are possible X and Y for X _||_ Y | Z1,...,Zn.
      Returns:
      The nodes that are possible X and Y for X _||_ Y | Z1,...,Zn.

    • getConditioningNodes

      public List<Node> getConditioningNodes()
      Returns the nodes that are possible Z1,...,Zn for X _||_ Y | Z1,...,Zn.
      Returns:
      The nodes that are possible Z1,...,Zn for X _||_ Y | Z1,...,Zn.

    • isCpdag

      public boolean isCpdag()
      Checks whether the given graph is a CPDAG (Completed Partially Directed Acyclic Graph).
      Returns:
      true if the graph is a CPDAG, false otherwise