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diff --git a/dev/_images/sphx_glr_plot_pc_alg_001.png b/dev/_images/sphx_glr_plot_pc_alg_001.png
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diff --git a/dev/_images/sphx_glr_plot_pc_alg_002.png b/dev/_images/sphx_glr_plot_pc_alg_002.png
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diff --git a/dev/_images/sphx_glr_plot_pc_alg_thumb.png b/dev/_images/sphx_glr_plot_pc_alg_thumb.png
index 5d7912a8..88b6e0c3 100644
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diff --git a/dev/_images/sphx_glr_plot_psifci_alg_002.png b/dev/_images/sphx_glr_plot_psifci_alg_002.png
index 9ca411c7..425d7f4b 100644
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diff --git a/dev/_images/sphx_glr_plot_psifci_alg_003.png b/dev/_images/sphx_glr_plot_psifci_alg_003.png
index 910b276c..3a680315 100644
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diff --git a/dev/_sources/auto_examples/plot_pc_alg.rst.txt b/dev/_sources/auto_examples/plot_pc_alg.rst.txt
index ba2ff777..20150b4a 100644
--- a/dev/_sources/auto_examples/plot_pc_alg.rst.txt
+++ b/dev/_sources/auto_examples/plot_pc_alg.rst.txt
@@ -150,7 +150,7 @@ are conditionally independent.
 
  .. code-block:: none
 
-
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1592.07it/s]
+
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1745.81it/s]
        x  ...  w
     0  0  ...  1
     1  1  ...  2
@@ -165,7 +165,7 @@ are conditionally independent.
     w    [1, 2, 0]
     dtype: object
 
-    <graphviz.graphs.Digraph object at 0x7fb23ef1ca30>
+    <graphviz.graphs.Digraph object at 0x7f388f26fee0>
 
 
 
@@ -330,7 +330,7 @@ always a possibility that the CI test makes a mistake.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.329 seconds)
+   **Total running time of the script:** ( 0 minutes  2.223 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_pc_alg.py:
diff --git a/dev/_sources/auto_examples/plot_psifci_alg.rst.txt b/dev/_sources/auto_examples/plot_psifci_alg.rst.txt
index 6e832e1f..88c6eecc 100644
--- a/dev/_sources/auto_examples/plot_psifci_alg.rst.txt
+++ b/dev/_sources/auto_examples/plot_psifci_alg.rst.txt
@@ -272,7 +272,7 @@ Figure 8 in :footcite:`Jaber2020causal`.
 
  .. code-block:: none
 
-    There are 155 edges in the resulting PAG
+    There are 149 edges in the resulting PAG
 
 
 
@@ -347,7 +347,7 @@ Visualize the graph without the F-nodes
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  37.926 seconds)
+   **Total running time of the script:** ( 3 minutes  25.196 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_psifci_alg.py:
diff --git a/dev/_sources/auto_examples/plot_score_alg.rst.txt b/dev/_sources/auto_examples/plot_score_alg.rst.txt
index 12eb381a..a5ce6b59 100644
--- a/dev/_sources/auto_examples/plot_score_alg.rst.txt
+++ b/dev/_sources/auto_examples/plot_score_alg.rst.txt
@@ -187,7 +187,7 @@ This will then induce a causal graph, which we can visualize.
 
  .. code-block:: none
 
-
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1843.65it/s]
+
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1925.98it/s]
        x  ...  w
     0  0  ...  1
     1  1  ...  2
@@ -333,7 +333,7 @@ step, corresponding to the causal graph inferred by SCORE.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.509 seconds)
+   **Total running time of the script:** ( 0 minutes  0.475 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_score_alg.py:
diff --git a/dev/_sources/auto_examples/prior_know_score.rst.txt b/dev/_sources/auto_examples/prior_know_score.rst.txt
index 91ae4964..80ca5992 100644
--- a/dev/_sources/auto_examples/prior_know_score.rst.txt
+++ b/dev/_sources/auto_examples/prior_know_score.rst.txt
@@ -137,7 +137,7 @@ This will then induce a causal graph, which we can visualize.
 
  .. code-block:: none
 
-
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1816.90it/s]
+
    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]
    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1967.08it/s]
        x  ...  w
     0  0  ...  1
     1  1  ...  2
@@ -273,7 +273,7 @@ For a detailed example on order-based discovery approaches, see this
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.835 seconds)
+   **Total running time of the script:** ( 0 minutes  0.806 seconds)
 
 
 .. _sphx_glr_download_auto_examples_prior_know_score.py:
diff --git a/dev/auto_examples/plot_pc_alg.html b/dev/auto_examples/plot_pc_alg.html
index c5b78501..86697c78 100644
--- a/dev/auto_examples/plot_pc_alg.html
+++ b/dev/auto_examples/plot_pc_alg.html
@@ -379,7 +379,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1592.07it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1745.81it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -394,7 +394,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 w    [1, 2, 0]
 dtype: object
 
-&lt;graphviz.graphs.Digraph object at 0x7fb23ef1ca30&gt;
+&lt;graphviz.graphs.Digraph object at 0x7f388f26fee0&gt;
 </pre></div>
 </div>
 </section>
@@ -488,7 +488,7 @@ <h2>Run structure learning algorithm<a class="headerlink" href="#run-structure-l
 <img src="../_images/sphx_glr_plot_pc_alg_002.png" srcset="../_images/sphx_glr_plot_pc_alg_002.png" alt="plot pc alg" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;ci_cpdag.png&#39;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.329 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.223 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pc-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/f1c5e835df0a73798ede9531ab050d9e/plot_pc_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_pc_alg.py</span></code></a></p>
diff --git a/dev/auto_examples/plot_psifci_alg.html b/dev/auto_examples/plot_psifci_alg.html
index b6b6a3a8..eb34d286 100644
--- a/dev/auto_examples/plot_psifci_alg.html
+++ b/dev/auto_examples/plot_psifci_alg.html
@@ -438,7 +438,7 @@ <h3>Analyze the results<a class="headerlink" href="#analyze-the-results" title="
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><a href="https://www.pywhy.org/pywhy-graphs/dev/generated/pywhy_graphs.networkx.MixedEdgeGraph.html#pywhy_graphs.networkx.MixedEdgeGraph.to_undirected" title="pywhy_graphs.networkx.MixedEdgeGraph.to_undirected" class="sphx-glr-backref-module-pywhy_graphs-networkx sphx-glr-backref-type-py-method"><span class="n">est_pag</span><span class="o">.</span><span class="n">to_undirected</span></a><span class="p">()</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span><span class="si">}</span><span class="s2"> edges in the resulting PAG&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 155 edges in the resulting PAG
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 149 edges in the resulting PAG
 </pre></div>
 </div>
 <p>Visualize the full graph including the F-node</p>
@@ -467,7 +467,7 @@ <h3>Analyze the results<a class="headerlink" href="#analyze-the-results" title="
 <img src="../_images/sphx_glr_plot_psifci_alg_003.png" srcset="../_images/sphx_glr_plot_psifci_alg_003.png" alt="plot psifci alg" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;psi_pag.png&#39;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  37.926 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  25.196 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-psifci-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/75f223a7812556342f0f4b79148d944b/plot_psifci_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_psifci_alg.py</span></code></a></p>
diff --git a/dev/auto_examples/plot_score_alg.html b/dev/auto_examples/plot_score_alg.html
index 4ee38e88..950912a7 100644
--- a/dev/auto_examples/plot_score_alg.html
+++ b/dev/auto_examples/plot_score_alg.html
@@ -411,7 +411,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1843.65it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1925.98it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -511,7 +511,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 One is the fully connected graph associated to the inferred topological order
 <code class="docutils literal notranslate"><span class="pre">[z,</span> <span class="pre">x,</span> <span class="pre">y,</span> <span class="pre">w]</span></code> of the graph nodes. The other is the sparser graph after the pruning
 step, corresponding to the causal graph inferred by SCORE.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.509 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.475 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-score-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7a0af3d327be5615defba776eff15f30/plot_score_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_score_alg.py</span></code></a></p>
diff --git a/dev/auto_examples/prior_know_score.html b/dev/auto_examples/prior_know_score.html
index 61364a99..32698cee 100644
--- a/dev/auto_examples/prior_know_score.html
+++ b/dev/auto_examples/prior_know_score.html
@@ -372,7 +372,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1816.90it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1967.08it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -450,7 +450,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 This example can be generalized to the case of <code class="docutils literal notranslate"><span class="pre">NoGAM</span></code>, <code class="docutils literal notranslate"><span class="pre">DAS</span></code>, and <code class="docutils literal notranslate"><span class="pre">CAM</span></code> methods.
 For a detailed example on order-based discovery approaches, see this
 <a class="reference internal" href="plot_score_alg.html#ex-score-algorithm"><span class="std std-ref">tutorial</span></a>.</p>
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 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-prior-know-score-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/54fa84cb158e29731e39a59414e303a3/prior_know_score.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">prior_know_score.py</span></code></a></p>
diff --git a/dev/searchindex.js b/dev/searchindex.js
index 2a8c5c8c..972ab324 100644
--- a/dev/searchindex.js
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"dodiscover.ci.ClassifierCITest.test"]], "classifiercmitest (class in dodiscover.ci)": [[18, "dodiscover.ci.ClassifierCMITest"]], "generate_train_test_data() (dodiscover.ci.classifiercmitest method)": [[18, "dodiscover.ci.ClassifierCMITest.generate_train_test_data"]], "test() (dodiscover.ci.classifiercmitest method)": [[18, "dodiscover.ci.ClassifierCMITest.test"]], "fisherzcitest (class in dodiscover.ci)": [[19, "dodiscover.ci.FisherZCITest"]], "test() (dodiscover.ci.fisherzcitest method)": [[19, "dodiscover.ci.FisherZCITest.test"]], "gsquarecitest (class in dodiscover.ci)": [[20, "dodiscover.ci.GSquareCITest"]], "test() (dodiscover.ci.gsquarecitest method)": [[20, "dodiscover.ci.GSquareCITest.test"]], "kernelcitest (class in dodiscover.ci)": [[21, "dodiscover.ci.KernelCITest"]], "test() (dodiscover.ci.kernelcitest method)": [[21, "dodiscover.ci.KernelCITest.test"]], "oracle (class in dodiscover.ci)": [[22, "dodiscover.ci.Oracle"]], "test() (dodiscover.ci.oracle method)": [[22, "dodiscover.ci.Oracle.test"]], "compute_kernel() (in module dodiscover.ci.kernel_utils)": [[23, "dodiscover.ci.kernel_utils.compute_kernel"]], "corrent_matrix() (in module dodiscover.ci.kernel_utils)": [[24, "dodiscover.ci.kernel_utils.corrent_matrix"]], "f_divergence_score() (in module dodiscover.ci.kernel_utils)": [[25, "dodiscover.ci.kernel_utils.f_divergence_score"]], "kl_divergence_score() (in module dodiscover.ci.kernel_utils)": [[26, "dodiscover.ci.kernel_utils.kl_divergence_score"]], "von_neumann_divergence() (in module dodiscover.ci.kernel_utils)": [[27, "dodiscover.ci.kernel_utils.von_neumann_divergence"]], "generate_knn_in_subspace() (in module dodiscover.ci.monte_carlo)": [[28, "dodiscover.ci.monte_carlo.generate_knn_in_subspace"]], "restricted_nbr_permutation() (in module dodiscover.ci.monte_carlo)": [[29, "dodiscover.ci.monte_carlo.restricted_nbr_permutation"]], "complete (dodiscover.constraint.conditioningsetselection attribute)": [[30, "dodiscover.constraint.ConditioningSetSelection.COMPLETE"]], "conditioningsetselection (class in dodiscover.constraint)": [[30, "dodiscover.constraint.ConditioningSetSelection"]], "nbrs (dodiscover.constraint.conditioningsetselection attribute)": [[30, "dodiscover.constraint.ConditioningSetSelection.NBRS"]], "nbrs_path (dodiscover.constraint.conditioningsetselection attribute)": [[30, "dodiscover.constraint.ConditioningSetSelection.NBRS_PATH"]], "pds (dodiscover.constraint.conditioningsetselection attribute)": [[30, "dodiscover.constraint.ConditioningSetSelection.PDS"]], "pds_path (dodiscover.constraint.conditioningsetselection attribute)": [[30, "dodiscover.constraint.ConditioningSetSelection.PDS_PATH"]], "fci (class in dodiscover.constraint)": [[31, "dodiscover.constraint.FCI"]], "evaluate_edge() (dodiscover.constraint.fci method)": [[31, "dodiscover.constraint.FCI.evaluate_edge"]], "learn_graph() (dodiscover.constraint.fci method)": [[31, "dodiscover.constraint.FCI.learn_graph"]], "learn_skeleton() (dodiscover.constraint.fci method)": [[31, "dodiscover.constraint.FCI.learn_skeleton"]], "orient_edges() (dodiscover.constraint.fci method)": [[31, "dodiscover.constraint.FCI.orient_edges"]], "orient_unshielded_triples() (dodiscover.constraint.fci method)": [[31, "dodiscover.constraint.FCI.orient_unshielded_triples"]], "learninterventionskeleton (class in dodiscover.constraint)": [[32, "dodiscover.constraint.LearnInterventionSkeleton"]], "ci_estimator (dodiscover.constraint.learninterventionskeleton attribute)": [[32, "dodiscover.constraint.LearnInterventionSkeleton.ci_estimator"]], "evaluate_edge() (dodiscover.constraint.learninterventionskeleton method)": [[32, "dodiscover.constraint.LearnInterventionSkeleton.evaluate_edge"]], "learnsemimarkovianskeleton (class in dodiscover.constraint)": [[33, "dodiscover.constraint.LearnSemiMarkovianSkeleton"]], "ci_estimator (dodiscover.constraint.learnsemimarkovianskeleton attribute)": [[33, "dodiscover.constraint.LearnSemiMarkovianSkeleton.ci_estimator"]], "evaluate_edge() (dodiscover.constraint.learnsemimarkovianskeleton method)": [[33, "dodiscover.constraint.LearnSemiMarkovianSkeleton.evaluate_edge"]], "learnskeleton (class in dodiscover.constraint)": [[34, "dodiscover.constraint.LearnSkeleton"]], "ci_estimator (dodiscover.constraint.learnskeleton attribute)": [[34, "dodiscover.constraint.LearnSkeleton.ci_estimator"]], "evaluate_edge() (dodiscover.constraint.learnskeleton method)": [[34, "dodiscover.constraint.LearnSkeleton.evaluate_edge"]], "pc (class in dodiscover.constraint)": [[35, "dodiscover.constraint.PC"]], "convert_skeleton_graph() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.convert_skeleton_graph"]], "evaluate_edge() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.evaluate_edge"]], "learn_graph() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.learn_graph"]], "learn_skeleton() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.learn_skeleton"]], "orient_edges() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.orient_edges"]], "orient_unshielded_triples() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.orient_unshielded_triples"]], "psifci (class in dodiscover.constraint)": [[36, "dodiscover.constraint.PsiFCI"]], "evaluate_edge() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.evaluate_edge"]], "learn_graph() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.learn_graph"]], "learn_skeleton() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.learn_skeleton"]], "orient_edges() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.orient_edges"]], "orient_unshielded_triples() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.orient_unshielded_triples"]], "context (class in dodiscover.context)": [[37, "dodiscover.context.Context"]], "__hash__ (dodiscover.context.context attribute)": [[37, "dodiscover.context.Context.__hash__"]], "add_state_variable() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.add_state_variable"]], "copy() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.copy"]], "get_augmented_nodes() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_augmented_nodes"]], "get_non_augmented_nodes() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_non_augmented_nodes"]], "get_params() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_params"]], "reverse_sigma_map() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.reverse_sigma_map"]], "set_params() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.set_params"]], "state_variable() (dodiscover.context.context method)": [[37, 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(dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.learn_graph"]], "prune() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.prune"]], "score() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.score"]], "nogam (class in dodiscover.toporder)": [[44, "dodiscover.toporder.NoGAM"]], "hessian() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.hessian"]], "hessian_diagonal() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.hessian_diagonal"]], "learn_graph() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.learn_graph"]], "prune() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.prune"]], "score() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.score"]], "score (class in dodiscover.toporder)": [[45, "dodiscover.toporder.SCORE"]], "hessian() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.hessian"]], "hessian_diagonal() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.hessian_diagonal"]], "learn_graph() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.learn_graph"]], "prune() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.prune"]], "score() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.score"]], "column (in module dodiscover.typing)": [[46, "dodiscover.typing.Column"]], "networkxgraph (in module dodiscover.typing)": [[47, "dodiscover.typing.NetworkxGraph"]], "separatingset (in module dodiscover.typing)": [[48, "dodiscover.typing.SeparatingSet"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["api", "auto_examples/plot_pc_alg", "auto_examples/plot_psifci_alg", "auto_examples/plot_score_alg", "auto_examples/prior_know_score", "conditional_independence", "constraint_causal_discovery", "generated/dodiscover.ContextBuilder", "generated/dodiscover.EquivalenceClass", "generated/dodiscover.Graph", "generated/dodiscover.InterventionalContextBuilder", "generated/dodiscover.cd.BaseConditionalDiscrepancyTest", "generated/dodiscover.cd.BregmanCDTest", "generated/dodiscover.cd.KernelCDTest", "generated/dodiscover.ci.BaseConditionalIndependenceTest", "generated/dodiscover.ci.CMITest", "generated/dodiscover.ci.CategoricalCITest", "generated/dodiscover.ci.ClassifierCITest", "generated/dodiscover.ci.ClassifierCMITest", "generated/dodiscover.ci.FisherZCITest", "generated/dodiscover.ci.GSquareCITest", "generated/dodiscover.ci.KernelCITest", "generated/dodiscover.ci.Oracle", "generated/dodiscover.ci.kernel_utils.compute_kernel", "generated/dodiscover.ci.kernel_utils.corrent_matrix", "generated/dodiscover.ci.kernel_utils.f_divergence_score", "generated/dodiscover.ci.kernel_utils.kl_divergence_score", "generated/dodiscover.ci.kernel_utils.von_neumann_divergence", "generated/dodiscover.ci.monte_carlo.generate_knn_in_subspace", "generated/dodiscover.ci.monte_carlo.restricted_nbr_permutation", "generated/dodiscover.constraint.ConditioningSetSelection", "generated/dodiscover.constraint.FCI", "generated/dodiscover.constraint.LearnInterventionSkeleton", "generated/dodiscover.constraint.LearnSemiMarkovianSkeleton", "generated/dodiscover.constraint.LearnSkeleton", "generated/dodiscover.constraint.PC", "generated/dodiscover.constraint.PsiFCI", "generated/dodiscover.context.Context", "generated/dodiscover.make_context", "generated/dodiscover.metrics.confusion_matrix_networks", "generated/dodiscover.metrics.structure_hamming_dist", "generated/dodiscover.metrics.toporder_divergence", "generated/dodiscover.toporder.CAM", "generated/dodiscover.toporder.DAS", "generated/dodiscover.toporder.NoGAM", "generated/dodiscover.toporder.SCORE", "generated/dodiscover.typing.Column", "generated/dodiscover.typing.NetworkxGraph", "generated/dodiscover.typing.SeparatingSet", "index", "installation", "tutorials/index", "tutorials/markovian/example-pc-algo", "use", "user_guide", "whats_new", "whats_new/_contributors", "whats_new/v0.1"], "filenames": ["api.rst", "auto_examples/plot_pc_alg.rst", "auto_examples/plot_psifci_alg.rst", "auto_examples/plot_score_alg.rst", "auto_examples/prior_know_score.rst", "conditional_independence.rst", "constraint_causal_discovery.rst", "generated/dodiscover.ContextBuilder.rst", "generated/dodiscover.EquivalenceClass.rst", "generated/dodiscover.Graph.rst", "generated/dodiscover.InterventionalContextBuilder.rst", "generated/dodiscover.cd.BaseConditionalDiscrepancyTest.rst", "generated/dodiscover.cd.BregmanCDTest.rst", "generated/dodiscover.cd.KernelCDTest.rst", "generated/dodiscover.ci.BaseConditionalIndependenceTest.rst", "generated/dodiscover.ci.CMITest.rst", "generated/dodiscover.ci.CategoricalCITest.rst", "generated/dodiscover.ci.ClassifierCITest.rst", "generated/dodiscover.ci.ClassifierCMITest.rst", "generated/dodiscover.ci.FisherZCITest.rst", "generated/dodiscover.ci.GSquareCITest.rst", "generated/dodiscover.ci.KernelCITest.rst", "generated/dodiscover.ci.Oracle.rst", "generated/dodiscover.ci.kernel_utils.compute_kernel.rst", "generated/dodiscover.ci.kernel_utils.corrent_matrix.rst", "generated/dodiscover.ci.kernel_utils.f_divergence_score.rst", "generated/dodiscover.ci.kernel_utils.kl_divergence_score.rst", "generated/dodiscover.ci.kernel_utils.von_neumann_divergence.rst", "generated/dodiscover.ci.monte_carlo.generate_knn_in_subspace.rst", "generated/dodiscover.ci.monte_carlo.restricted_nbr_permutation.rst", "generated/dodiscover.constraint.ConditioningSetSelection.rst", "generated/dodiscover.constraint.FCI.rst", "generated/dodiscover.constraint.LearnInterventionSkeleton.rst", "generated/dodiscover.constraint.LearnSemiMarkovianSkeleton.rst", "generated/dodiscover.constraint.LearnSkeleton.rst", "generated/dodiscover.constraint.PC.rst", "generated/dodiscover.constraint.PsiFCI.rst", "generated/dodiscover.context.Context.rst", "generated/dodiscover.make_context.rst", "generated/dodiscover.metrics.confusion_matrix_networks.rst", "generated/dodiscover.metrics.structure_hamming_dist.rst", "generated/dodiscover.metrics.toporder_divergence.rst", "generated/dodiscover.toporder.CAM.rst", "generated/dodiscover.toporder.DAS.rst", "generated/dodiscover.toporder.NoGAM.rst", "generated/dodiscover.toporder.SCORE.rst", "generated/dodiscover.typing.Column.rst", "generated/dodiscover.typing.NetworkxGraph.rst", "generated/dodiscover.typing.SeparatingSet.rst", "index.rst", "installation.md", "tutorials/index.rst", "tutorials/markovian/example-pc-algo.ipynb", "use.rst", "user_guide.rst", "whats_new.rst", "whats_new/_contributors.rst", "whats_new/v0.1.rst"], "titles": ["API", "Basic causal discovery with DoDiscover using the PC algorithm", "Causal discovery with interventional data - Sachs dataset", "Order-based algorithms for causal discovery from observational data without latent confounders", "Using prior knowledge in order-based algorithms for causal discovery", "<span class=\"section-number\">2. </span>Independence", "<span class=\"section-number\">1. </span>Constraint-based causal discovery", "dodiscover.ContextBuilder", "dodiscover.EquivalenceClass", "dodiscover.Graph", "dodiscover.InterventionalContextBuilder", "dodiscover.cd.BaseConditionalDiscrepancyTest", "<span class=\"section-number\">3.3.1. </span>dodiscover.cd.BregmanCDTest", "<span class=\"section-number\">3.2.1. </span>dodiscover.cd.KernelCDTest", "dodiscover.ci.BaseConditionalIndependenceTest", "<span class=\"section-number\">2.1.1. </span>dodiscover.ci.CMITest", "dodiscover.ci.CategoricalCITest", "<span class=\"section-number\">2.5.1. </span>dodiscover.ci.ClassifierCITest", "<span class=\"section-number\">2.1.2. </span>dodiscover.ci.ClassifierCMITest", "<span class=\"section-number\">2.2.1. </span>dodiscover.ci.FisherZCITest", "<span class=\"section-number\">2.3.1. </span>dodiscover.ci.GSquareCITest", "<span class=\"section-number\">2.4.1. </span>dodiscover.ci.KernelCITest", "dodiscover.ci.Oracle", "dodiscover.ci.kernel_utils.compute_kernel", "dodiscover.ci.kernel_utils.corrent_matrix", "dodiscover.ci.kernel_utils.f_divergence_score", "dodiscover.ci.kernel_utils.kl_divergence_score", "dodiscover.ci.kernel_utils.von_neumann_divergence", "dodiscover.ci.monte_carlo.generate_knn_in_subspace", "dodiscover.ci.monte_carlo.restricted_nbr_permutation", "<span class=\"section-number\">1.5.1. </span>dodiscover.constraint.ConditioningSetSelection", "dodiscover.constraint.FCI", "dodiscover.constraint.LearnInterventionSkeleton", "dodiscover.constraint.LearnSemiMarkovianSkeleton", "dodiscover.constraint.LearnSkeleton", "dodiscover.constraint.PC", "<span class=\"section-number\">1.4.1. </span>dodiscover.constraint.PsiFCI", "dodiscover.context.Context", "dodiscover.make_context", "dodiscover.metrics.confusion_matrix_networks", "dodiscover.metrics.structure_hamming_dist", "dodiscover.metrics.toporder_divergence", "dodiscover.toporder.CAM", "dodiscover.toporder.DAS", "dodiscover.toporder.NoGAM", "dodiscover.toporder.SCORE", "dodiscover.typing.Column", "dodiscover.typing.NetworkxGraph", "dodiscover.typing.SeparatingSet", "<strong>dodiscover</strong>", "Installation", "Tutorials", "PC algorithm for causal discovery from observational data without latent confounders", "Examples and Tutorials using DoDiscover", "User guide: contents", "Release History", "&lt;no title&gt;", "What\u2019s new?"], "terms": {"dodiscov": [0, 2, 3, 4, 6, 50, 52, 54, 55, 57], "thi": [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 17, 18, 22, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 49, 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"partial-pearson-correlation"]], "Discrete, Categorical and Binary Data": [[5, "discrete-categorical-and-binary-data"], [5, "id6"]], "Kernel-Approaches": [[5, "kernel-approaches"], [5, "id7"]], "Classifier-based Approaches": [[5, "classifier-based-approaches"]], "Conditional Discrepancy": [[5, "conditional-discrepancy"]], "Bregman-Divergences": [[5, "bregman-divergences"]], "Constraint-based causal discovery": [[6, "module-dodiscover.constraint"]], "Fundamental Assumptions of Constraint-Based Causal Discovery": [[6, "fundamental-assumptions-of-constraint-based-causal-discovery"]], "(Non-parametric) Markovian SCMs with Observational Data": [[6, "non-parametric-markovian-scms-with-observational-data"]], "(Non-parametric) Semi-Markovian SCMs with Observational Data": [[6, "non-parametric-semi-markovian-scms-with-observational-data"]], "(Non-parametric) SCMs with Interventional Data": [[6, "non-parametric-scms-with-interventional-data"]], "Choosing the conditioning sets": [[6, 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"dodiscover-ci-baseconditionalindependencetest"]], "Examples using dodiscover.ci.BaseConditionalIndependenceTest": [[14, "examples-using-dodiscover-ci-baseconditionalindependencetest"]], "dodiscover.ci.CMITest": [[15, "dodiscover-ci-cmitest"]], "dodiscover.ci.CategoricalCITest": [[16, "dodiscover-ci-categoricalcitest"]], "dodiscover.ci.ClassifierCITest": [[17, "dodiscover-ci-classifiercitest"]], "dodiscover.ci.ClassifierCMITest": [[18, "dodiscover-ci-classifiercmitest"]], "dodiscover.ci.FisherZCITest": [[19, "dodiscover-ci-fisherzcitest"]], "dodiscover.ci.GSquareCITest": [[20, "dodiscover-ci-gsquarecitest"]], "Examples using dodiscover.ci.GSquareCITest": [[20, "examples-using-dodiscover-ci-gsquarecitest"]], "dodiscover.ci.KernelCITest": [[21, "dodiscover-ci-kernelcitest"]], "dodiscover.ci.Oracle": [[22, "dodiscover-ci-oracle"]], "Examples using dodiscover.ci.Oracle": [[22, "examples-using-dodiscover-ci-oracle"]], "dodiscover.ci.kernel_utils.compute_kernel": [[23, 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"dodiscover.toporder.NoGAM": [[44, "dodiscover-toporder-nogam"]], "dodiscover.toporder.SCORE": [[45, "dodiscover-toporder-score"]], "Examples using dodiscover.toporder.SCORE": [[45, "examples-using-dodiscover-toporder-score"]], "dodiscover.typing.Column": [[46, "dodiscover-typing-column"]], "dodiscover.typing.NetworkxGraph": [[47, "dodiscover-typing-networkxgraph"]], "dodiscover.typing.SeparatingSet": [[48, "dodiscover-typing-separatingset"]], "dodiscover": [[49, "dodiscover"]], "Contents": [[49, "contents"]], "Getting started:": [[49, null]], "Team": [[49, "team"]], "Indices and tables": [[49, "indices-and-tables"]], "Installation": [[50, "installation"]], "Tutorials": [[51, "tutorials"]], "Basic causal discovery models without latent confounders": [[51, "basic-causal-discovery-models-without-latent-confounders"]], "PC algorithm for causal discovery from observational data without latent confounders": [[52, 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"dodiscover.constraint.LearnSemiMarkovianSkeleton.ci_estimator"]], "evaluate_edge() (dodiscover.constraint.learnsemimarkovianskeleton method)": [[33, "dodiscover.constraint.LearnSemiMarkovianSkeleton.evaluate_edge"]], "learnskeleton (class in dodiscover.constraint)": [[34, "dodiscover.constraint.LearnSkeleton"]], "ci_estimator (dodiscover.constraint.learnskeleton attribute)": [[34, "dodiscover.constraint.LearnSkeleton.ci_estimator"]], "evaluate_edge() (dodiscover.constraint.learnskeleton method)": [[34, "dodiscover.constraint.LearnSkeleton.evaluate_edge"]], "pc (class in dodiscover.constraint)": [[35, "dodiscover.constraint.PC"]], "convert_skeleton_graph() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.convert_skeleton_graph"]], "evaluate_edge() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.evaluate_edge"]], "learn_graph() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.learn_graph"]], "learn_skeleton() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.learn_skeleton"]], "orient_edges() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.orient_edges"]], "orient_unshielded_triples() (dodiscover.constraint.pc method)": [[35, "dodiscover.constraint.PC.orient_unshielded_triples"]], "psifci (class in dodiscover.constraint)": [[36, "dodiscover.constraint.PsiFCI"]], "evaluate_edge() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.evaluate_edge"]], "learn_graph() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.learn_graph"]], "learn_skeleton() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.learn_skeleton"]], "orient_edges() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.orient_edges"]], "orient_unshielded_triples() (dodiscover.constraint.psifci method)": [[36, "dodiscover.constraint.PsiFCI.orient_unshielded_triples"]], "context (class in dodiscover.context)": [[37, "dodiscover.context.Context"]], "__hash__ (dodiscover.context.context attribute)": [[37, "dodiscover.context.Context.__hash__"]], "add_state_variable() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.add_state_variable"]], "copy() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.copy"]], "get_augmented_nodes() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_augmented_nodes"]], "get_non_augmented_nodes() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_non_augmented_nodes"]], "get_params() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.get_params"]], "reverse_sigma_map() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.reverse_sigma_map"]], "set_params() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.set_params"]], "state_variable() (dodiscover.context.context method)": [[37, "dodiscover.context.Context.state_variable"]], "make_context() (in module dodiscover)": [[38, "dodiscover.make_context"]], "confusion_matrix_networks() (in module dodiscover.metrics)": [[39, "dodiscover.metrics.confusion_matrix_networks"]], "structure_hamming_dist() (in module dodiscover.metrics)": [[40, "dodiscover.metrics.structure_hamming_dist"]], "toporder_divergence() (in module dodiscover.metrics)": [[41, "dodiscover.metrics.toporder_divergence"]], "cam (class in dodiscover.toporder)": [[42, "dodiscover.toporder.CAM"]], "learn_graph() (dodiscover.toporder.cam method)": [[42, "dodiscover.toporder.CAM.learn_graph"]], "prune() (dodiscover.toporder.cam method)": [[42, "dodiscover.toporder.CAM.prune"]], "das (class in dodiscover.toporder)": [[43, "dodiscover.toporder.DAS"]], "hessian() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.hessian"]], "hessian_diagonal() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.hessian_diagonal"]], "learn_graph() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.learn_graph"]], "prune() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.prune"]], "score() (dodiscover.toporder.das method)": [[43, "dodiscover.toporder.DAS.score"]], "nogam (class in dodiscover.toporder)": [[44, "dodiscover.toporder.NoGAM"]], "hessian() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.hessian"]], "hessian_diagonal() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.hessian_diagonal"]], "learn_graph() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.learn_graph"]], "prune() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.prune"]], "score() (dodiscover.toporder.nogam method)": [[44, "dodiscover.toporder.NoGAM.score"]], "score (class in dodiscover.toporder)": [[45, "dodiscover.toporder.SCORE"]], "hessian() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.hessian"]], "hessian_diagonal() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.hessian_diagonal"]], "learn_graph() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.learn_graph"]], "prune() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.prune"]], "score() (dodiscover.toporder.score method)": [[45, "dodiscover.toporder.SCORE.score"]], "column (in module dodiscover.typing)": [[46, "dodiscover.typing.Column"]], "networkxgraph (in module dodiscover.typing)": [[47, "dodiscover.typing.NetworkxGraph"]], "separatingset (in module dodiscover.typing)": [[48, "dodiscover.typing.SeparatingSet"]]}})
\ No newline at end of file