minor fixes to interventional distribution notebook (#553)

* mutilation -> mutation

* remove mention of dataframes

Author: drbenvincent

examples/causal_inference/interventional_distribution.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f2e8530c-5ba0-4041-a309-18919d5d0533",
    "metadata": {
@@ -12,7 +13,7 @@
    },
    "source": [
     "(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",
@@ -22,6 +23,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8f973a4f-def4-4fb6-b166-2aefbdf5eee2",
    "metadata": {
@@ -44,6 +46,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ed816fbf-320c-44f7-a711-cb4750d86e24",
    "metadata": {},
@@ -75,6 +78,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e2441116-c8a5-41af-be95-9eeddf2b0e2e",
    "metadata": {
@@ -136,6 +140,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "782d6135-2e59-4462-8b9c-9832a58751ef",
    "metadata": {
@@ -259,6 +264,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f618f053-266b-4006-a521-f892cb8519e1",
    "metadata": {
@@ -281,6 +287,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ca2ed504-ffda-4f1d-8d18-cdb06608202b",
    "metadata": {
@@ -301,6 +308,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "83b8106f-c9e1-4277-ba38-f13b03a33ef6",
    "metadata": {
@@ -466,6 +474,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "56832ddc-ef61-4dbf-b46f-b6ef2ae8820e",
    "metadata": {
@@ -482,6 +491,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "24792fb8-3e40-41b5-a26a-4a2d4da052ed",
    "metadata": {
@@ -503,6 +513,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "aac4d49b-6a1e-4d77-a9c7-205050bebb9f",
    "metadata": {
@@ -572,6 +583,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b70e42b0-61a1-4c62-8dd9-d973d60d7946",
    "metadata": {
@@ -727,6 +739,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3bb5134c-f476-4b8f-9338-1ed07e1fbb13",
    "metadata": {
@@ -763,6 +776,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e2d1a7ca-1948-4165-92aa-3713df60b73b",
    "metadata": {},
@@ -773,6 +787,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "453edf32-48d2-4d7f-96fb-6fb61851de9e",
    "metadata": {
@@ -783,7 +798,7 @@
     "tags": []
    },
    "source": [
-    "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."
    ]
   },
   {
@@ -1095,6 +1110,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "07b66542-dd10-43b1-9dfb-5b438f756736",
    "metadata": {
@@ -1164,6 +1180,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6625a68d-e19e-4490-b486-29e3bff42716",
    "metadata": {
@@ -1178,6 +1195,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b44eef67-7ad3-46cc-b18c-e589b9fd271b",
    "metadata": {
@@ -1192,6 +1210,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2d30eb22-2560-495a-8af2-f9c9fe04848a",
    "metadata": {},
@@ -1219,6 +1238,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "99ccc753-e3ef-4834-b02f-4a8d82749fe5",
    "metadata": {},
@@ -1227,6 +1247,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d0896e42-ee27-4c37-a065-7c78d6f6c0e2",
    "metadata": {
@@ -1321,6 +1342,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "95844edc-2e47-47a1-986a-c6e0b30386c0",
    "metadata": {},
@@ -1471,6 +1493,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "688de710-654d-4905-a328-81941aaa1fe6",
    "metadata": {
@@ -1485,6 +1508,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "01ba8784-cd8b-45e8-b9ec-1fd6d56b010a",
    "metadata": {
@@ -1532,6 +1556,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d9d4fe62-b694-4a90-9e7f-21344f69fcf6",
    "metadata": {
@@ -1599,6 +1624,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f546f9e2-0f9b-4eaa-93e3-b5887b757a37",
    "metadata": {
@@ -1675,6 +1701,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9c4122ee-9709-4c9c-b0eb-a4c20e1b7f3a",
    "metadata": {
@@ -1761,6 +1788,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dec5c8d6-e562-47ca-a263-2e8269704d04",
    "metadata": {
@@ -1775,6 +1803,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e546b0ab-dd66-4c20-b814-c8cd1bc6c710",
    "metadata": {
@@ -1797,6 +1826,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f17a9b3b-a3c2-4919-893b-569049db03d6",
    "metadata": {
@@ -1812,6 +1842,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ecbb878d-531b-4c96-afe2-c39f928b9162",
    "metadata": {},
@@ -1824,6 +1855,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9fd548d0-5977-4a19-935a-506e86063887",
    "metadata": {},
@@ -1871,6 +1903,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "62e8040f-452c-4a11-90f5-fa94eb03c971",
    "metadata": {

examples/causal_inference/interventional_distribution.myst.md

@@ -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
@@ -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
 ---