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‎dev/_sources/auto_examples/plot_pc_alg.rst.txt

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.. code-block:: none
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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, 1588.00it/s]
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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, 1703.62it/s]
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.. rst-class:: sphx-glr-timing
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.. _sphx_glr_download_auto_examples_plot_pc_alg.py:

‎dev/_sources/auto_examples/plot_psifci_alg.rst.txt

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There are 155 edges in the resulting PAG
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There are 157 edges in the resulting PAG
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.. _sphx_glr_download_auto_examples_plot_psifci_alg.py:

‎dev/_sources/auto_examples/plot_score_alg.rst.txt

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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, 1944.28it/s]
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.. _sphx_glr_download_auto_examples_plot_score_alg.py:

‎dev/_sources/auto_examples/prior_know_score.rst.txt

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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, 1880.22it/s]
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.. _sphx_glr_download_auto_examples_prior_know_score.py:

‎dev/auto_examples/plot_pc_alg.html

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</pre></div>
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</div>
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</section>
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<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;
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pc-alg-py">
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<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>

‎dev/auto_examples/plot_psifci_alg.html

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<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>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 155 edges in the resulting PAG
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 157 edges in the resulting PAG
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<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;
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 41.070 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-psifci-alg-py">
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<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>

‎dev/auto_examples/plot_score_alg.html

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Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1944.28it/s]
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One is the fully connected graph associated to the inferred topological order
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<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
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step, corresponding to the causal graph inferred by SCORE.</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.487 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-score-alg-py">
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<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>

‎dev/auto_examples/prior_know_score.html

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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.
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For a detailed example on order-based discovery approaches, see this
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<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">
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<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>

‎dev/generated/dodiscover.ContextBuilder.html

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<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v3.0.0.dev0+1178.g236d89b856)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
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‎dev/generated/dodiscover.InterventionalContextBuilder.html

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<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v3.0.0.dev0+1176.gf3ad4d50ae)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
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<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v3.0.0.dev0+1178.g236d89b856)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
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