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merged 2 commits into from
Jul 4, 2023
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HOTFIX: typo in title of new notebook, #552 #553

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362727b
mutilation -> mutation
drbenvincent Jul 4, 2023
5e5cf7b
remove mention of dataframes
drbenvincent Jul 4, 2023
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37 changes: 35 additions & 2 deletions examples/causal_inference/interventional_distribution.ipynb
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@@ -1,6 +1,7 @@
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Expand All @@ -12,7 +13,7 @@
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"(interventional_distribution)=\n",
"# Interventional distributions and graph mutilation with the do-operator\n",
"# Interventional distributions and graph mutation with the do-operator\n",
"\n",
":::{post} July, 2023\n",
":tags: causal inference, do-operator, graph mutation\n",
Expand All @@ -22,6 +23,7 @@
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Expand All @@ -44,6 +46,7 @@
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Expand Down Expand Up @@ -75,6 +78,7 @@
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Expand Down Expand Up @@ -136,6 +140,7 @@
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Expand Down Expand Up @@ -259,6 +264,7 @@
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Expand All @@ -281,6 +287,7 @@
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Expand All @@ -301,6 +308,7 @@
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Expand Down Expand Up @@ -466,6 +474,7 @@
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Expand All @@ -482,6 +491,7 @@
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Expand All @@ -503,6 +513,7 @@
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Expand Down Expand Up @@ -572,6 +583,7 @@
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Expand Down Expand Up @@ -727,6 +739,7 @@
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Expand Down Expand Up @@ -763,6 +776,7 @@
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Expand All @@ -773,6 +787,7 @@
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Expand All @@ -783,7 +798,7 @@
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"However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll package the data up in `DataFrame`'s for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above."
"However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll extract the samples for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above."
]
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Expand Down Expand Up @@ -1095,6 +1110,7 @@
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Expand Down Expand Up @@ -1164,6 +1180,7 @@
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Expand All @@ -1178,6 +1195,7 @@
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Expand All @@ -1192,6 +1210,7 @@
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Expand Down Expand Up @@ -1219,6 +1238,7 @@
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Expand All @@ -1227,6 +1247,7 @@
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Expand Down Expand Up @@ -1321,6 +1342,7 @@
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Expand Down Expand Up @@ -1471,6 +1493,7 @@
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Expand All @@ -1485,6 +1508,7 @@
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Expand Down Expand Up @@ -1532,6 +1556,7 @@
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Expand Down Expand Up @@ -1599,6 +1624,7 @@
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Expand Down Expand Up @@ -1675,6 +1701,7 @@
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Expand Down Expand Up @@ -1761,6 +1788,7 @@
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Expand All @@ -1775,6 +1803,7 @@
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Expand All @@ -1797,6 +1826,7 @@
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Expand All @@ -1812,6 +1842,7 @@
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Expand All @@ -1824,6 +1855,7 @@
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Expand Down Expand Up @@ -1871,6 +1903,7 @@
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Expand Down
4 changes: 2 additions & 2 deletions examples/causal_inference/interventional_distribution.myst.md
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ kernelspec:
+++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}

(interventional_distribution)=
# Interventional distributions and graph mutilation with the do-operator
# Interventional distributions and graph mutation with the do-operator

:::{post} July, 2023
:tags: causal inference, do-operator, graph mutation
Expand Down Expand Up @@ -329,7 +329,7 @@ These code snippets are important because they define identical joint distributi

+++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}

However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll package the data up in `DataFrame`'s for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above.
However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll extract the samples for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above.

```{code-cell} ipython3
---
Expand Down