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Credici: Credal Inference for Causal Inference

Home Page: https://credici.readthedocs.io/

License: GNU Lesser General Public License v3.0

Java 100.00%

causality causal-inference causal-models credal imprecise-probability probabilistic-graphical-models

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credici's Issues

ApproxLP is not working with prob=0

For example, in the party example with X4.

Extend unit tests

Create or complete the unit tests for the following classes or functionalities:

SCM

Non quasi / non
Endog exo links
getEmpiricalMap
getExogenousTreewidth
C-components

EquationBuilder
CausalVE
CredalCausalApproxLP
CredalCausalVE
ExactCredalBuilder
EMCredalBuilder
Utilities

Intervened endogenous variables are considered as exogenous

        StructuralCausalModel causalModel = Party.buildModel();
        SparseModel vcredal = causalModel.toVCredal(causalModel.getEmpiricalProbs());
        int[] U = CausalInfo.of((SparseModel) vcredal.counterfactual_do(1,0)).getExogenousVars();
        System.out.println(Arrays.toString(U)); // [1, 4, 5, 6, 7] while should be [4, 5, 6, 7]

RandomUtilities from old crema

While developing #24, create a RandomUtilities class with an identical behaviour to the random generators in crema <0.2

Credal Network builder (from causal model)

Markovianity check

Method to check if a SCM is markovian, quasi-markovian...

set Elimination order in CredalCausalVE

This feature have been removed because of counterfactual queries

Functions to compute exogenous sizes (min and max)

Adapt VCredal parser to UAI Endianness

SCMs builders

Define API for building SCM:

Option 1: SEs + empirical model
Option 2: SEs + empirical DAG + List of factors

Causal queries with approxLP using crema 0.2.x

In branch dev-crema020-iss24 approxLP is not working for calculating causal queries due to an unfeasible exception. This is based on crema 0.2.2. Check the following examples where it fails.

https://github.com/IDSIA/credici/blob/dev-crema020-iss24/examples/not_working/EquationlessFromFile.java

https://github.com/IDSIA/credici/blob/dev-crema020-iss24/examples/not_working/PartyCounterfactuals.java

https://github.com/IDSIA/credici/blob/dev-crema020-iss24/examples/not_working/Queries.java

Build quasi-markovian SCM in equation less case

Extract empirical factors from Bayesian network

When building a credal network from a causal model, empirical distributions can be obtained
from a Bayesian network (instead of being provided in a list). This network can have any topology, but
it should be defined over the set of endogenous variables.


        // Causal DAG
        SparseDirectedAcyclicGraph causalDAG = new SparseDirectedAcyclicGraph();
        IntStream.range(0,4).forEach(x -> causalDAG.addVariable(x));

        causalDAG.addLink(0,1);
        causalDAG.addLink(0,2);
        causalDAG.addLink(1,3);
        causalDAG.addLink(2,3);
        
        // Empirical Bayesian Network
        BayesianNetwork bnet = (BayesianNetwork) IO.read("./models/party-empirical-rev.uai") ;


        // Build causal model (with unobserved U)
        StructuralCausalModel causalModel = CausalBuilder.of(causalDAG, 2).build();

        int[] x = causalModel.getEndogenousVars();

        SparseModel hcredal = CredalBuilder.of(causalModel)
                                .setEmpirical(bnet)
                                .setToHalfSpace()
                                .build();


    BayesianNetwork bnet = (BayesianNetwork) IO.read(prj_folder+"models/party-empirical.uai");

    // Build the causal model
    StructuralCausalModel causalModel = CausalBuilder.of(bnet).build();

    // Get the endogenous variables
    int[] x = causalModel.getEndogenousVars();

    // Convert the causal models into credal networks
    SparseModel vcredal = causalModel.toVCredal(bnet.getFactors());
    SparseModel hcredal = causalModel.toHCredal(bnet.getFactors());


    /////// Causal query P(X3 | do(X2 = 1))

    //Exact inference
    CredalCausalVE infExact = new CredalCausalVE(vcredal);

    /////// Counterfactual query P(X3' | do(X2 = 1), X2=0)

    //Exact inference

     SparseModel infModel = (SparseModel) infExact.counterfactualQuery()
             .setTarget(x[3])
             .setIntervention(x[1],1)
             .setEvidence(x[1], 0)
             .getInferenceModel();

     vcredal.getFactor(2)
     infModel.getFactor(10)

    /*

    // correct:
    K(vars[10]|[0, 0]) [1.0, 0.0]
    K(vars[10]|[0, 1]) [1.0, 0.0]
    K(vars[10]|[0, 2]) [0.0, 1.0]
    K(vars[10]|[0, 3]) [0.0, 1.0]
    K(vars[10]|[1, 0]) [1.0, 0.0]
    K(vars[10]|[1, 1]) [0.0, 1.0]
    K(vars[10]|[1, 2]) [1.0, 0.0]
    K(vars[10]|[1, 3]) [0.0, 1.0]

    // wrong
    K(vars[10]|[0, 0]) [1.0, 0.0]
    K(vars[10]|[1, 0]) [0.0, 1.0]
    K(vars[10]|[2, 0]) [1.0, 0.0]
    K(vars[10]|[3, 0]) [1.0, 0.0]
    K(vars[10]|[0, 1]) [1.0, 0.0]
    K(vars[10]|[1, 1]) [0.0, 1.0]
    K(vars[10]|[2, 1]) [0.0, 1.0]
    K(vars[10]|[3, 1]) [0.0, 1.0]

     */

Exception in thread "main" org.apache.commons.math3.optim.linear.NoFeasibleSolutionException: no feasible solution
	at org.apache.commons.math3.optim.linear.SimplexSolver.solvePhase1(SimplexSolver.java:355)
	at org.apache.commons.math3.optim.linear.SimplexSolver.doOptimize(SimplexSolver.java:380)
	at org.apache.commons.math3.optim.linear.SimplexSolver.doOptimize(SimplexSolver.java:65)
	at org.apache.commons.math3.optim.BaseOptimizer.optimize(BaseOptimizer.java:153)
	at org.apache.commons.math3.optim.BaseMultivariateOptimizer.optimize(BaseMultivariateOptimizer.java:65)
	at org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer.optimize(MultivariateOptimizer.java:63)
	at org.apache.commons.math3.optim.linear.LinearOptimizer.optimize(LinearOptimizer.java:94)
	at org.apache.commons.math3.optim.linear.SimplexSolver.optimize(SimplexSolver.java:154)
	at ch.idsia.crema.solver.commons.Simplex.solve(Simplex.java:55)
	at ch.idsia.crema.factor.convert.SeparateLinearToRandomBayesian.apply(SeparateLinearToRandomBayesian.java:63)
	at ch.idsia.crema.inference.approxlp.Neighbourhood.random(Neighbourhood.java:63)
	at ch.idsia.crema.inference.approxlp.Neighbourhood.random(Neighbourhood.java:47)
	at ch.idsia.crema.inference.approxlp.Inference.runSearcher(Inference.java:84)
	at ch.idsia.crema.inference.approxlp.Inference.query(Inference.java:57)
	at ch.idsia.crema.inference.approxlp.Inference.query(Inference.java:18)
	at ch.idsia.credici.inference.CredalCausalAproxLP.query(CredalCausalAproxLP.java:85)
	at ch.idsia.credici.inference.CredalCausalAproxLP.query(CredalCausalAproxLP.java:18)
	at ch.idsia.credici.inference.CausalInference.doQuery(CausalInference.java:36)
	at EquationlessFromFile.main(EquationlessFromFile.java:31)

error when running vertex VE:

Exception in thread "main" org.apache.commons.math3.optim.linear.NoFeasibleSolutionException: no feasible solution
	at ch.idsia.credici.model.CredalBuilder.build(CredalBuilder.java:120)
	at ch.idsia.credici.model.StructuralCausalModel.toVCredal(StructuralCausalModel.java:635)
	at ch.idsia.credici.inference.CredalCausalVE.<init>(CredalCausalVE.java:37)
	at EquationlessFromFile.main(EquationlessFromFile.java:30)

This could be due to a bad order of the variables from which coefficients/values are obtained

Crema 0.2.0 compatibility

branch 'dev-crema020-iss24'

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