Credici is an open-source library that allows to use credal inference methods
for causal analysis:

import ch.idsia.credici.inference.CredalCausalApproxLP;
import ch.idsia.credici.inference.CredalCausalVE;
import ch.idsia.credici.model.StructuralCausalModel;
import ch.idsia.crema.IO;
import ch.idsia.crema.factor.credal.linear.IntervalFactor;
import ch.idsia.crema.factor.credal.vertex.VertexFactor;
import ch.idsia.crema.model.graphical.specialized.BayesianNetwork;
import gnu.trove.map.TIntIntMap;
import gnu.trove.map.hash.TIntIntHashMap;
import java.io.IOException;
public class EquationlessFromFile {
    public static void main(String[] args) throws IOException, InterruptedException {
        // Load the empirical model
        String fileName = "./models/simple-bayes.uai";
        BayesianNetwork bnet = (BayesianNetwork) IO.read(fileName);
        // Get the markovian equationless SCM
        StructuralCausalModel causalModel = StructuralCausalModel.of(bnet);
        // Set query
        TIntIntMap intervention = new TIntIntHashMap();
        intervention.put(0,1);
        int target = 1;
        // Approx inference
        CredalCausalAproxLP inf = new CredalCausalAproxLP(causalModel, bnet.getFactors());
        IntervalFactor res = inf.doQuery(target, intervention);
        System.out.println(res);
        //Exact inference
        CredalCausalVE inf2 = new CredalCausalVE(causalModel, bnet.getFactors());
        VertexFactor res2 = inf2.doQuery(target, intervention);
        System.out.println(res2);
    }
}

Installation

Add the following code in the pom.xml of your project:

    <repositories>
        <repository>
            <id>cremaRepo</id>
            <url>https://raw.github.com/idsia/crema/mvn-repo/</url>
        </repository>
    </repositories>
    <dependencies>
        <dependency>
            <groupId>ch.idsia</groupId>
            <artifactId>credici</artifactId>
            <version>0.1.4</version>
            <scope>compile</scope>
        </dependency>
    </dependencies>