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diff --git a/examples/case_studies/CFA_SEM.ipynb b/examples/case_studies/CFA_SEM.ipynb
diff --git a/examples/case_studies/CFA_SEM.myst.md b/examples/case_studies/CFA_SEM.myst.md
diff --git a/examples/case_studies/structural_model_sem.png b/examples/case_studies/structural_model_sem.png
diff --git a/examples/causal_inference/GLM-simpsons-paradox.ipynb b/examples/causal_inference/GLM-simpsons-paradox.ipynb
diff --git a/...inear_models/GLM-simpsons-paradox.myst.md → ...al_inference/GLM-simpsons-paradox.myst.md b/...inear_models/GLM-simpsons-paradox.myst.md → ...al_inference/GLM-simpsons-paradox.myst.md
@@ -11,10 +11,10 @@ kernelspec:
 ---
 
 (GLM-simpsons-paradox)=
-# Simpson's paradox and mixed models
+# Simpson's paradox
 
 :::{post} September, 2024
-:tags: regression, hierarchical model, linear model, posterior predictive, Simpson's paradox 
+:tags: regression, hierarchical model, linear model, posterior predictive, causal inference, Simpson's paradox 
 :category: beginner
 :author: Benjamin T. Vincent
 :::
@@ -25,7 +25,9 @@ kernelspec:
 
 ![](https://upload.wikimedia.org/wikipedia/commons/f/fb/Simpsons_paradox_-_animation.gif)
 
-This paradox can be resolved by assuming a causal DAG which includes how the main predictor variable _and_ group membership influence the outcome variable. We demonstrate an example where we _don't_ incorporate group membership (so our causal DAG is wrong, or in other words our model is misspecified). We then show 2 ways to resolve this by including group membership as causal influence upon the outcome variable. This is shown in an unpooled model (which we could also call a fixed effects model) and a hierarchical model (which we could also call a mixed effects model).
+Another way of describing this is that we wish to estimate the causal relationship $x \rightarrow y$. The seemingly obvious approach of modelling `y ~ 1 + x` will lead us to conclude (in the situation above) that increasing $x$ causes $y$ to decrease (see Model 1 below). However, the relationship between $x$ and $y$ is confounded by a group membership variable $group$. This group membership variable is not included in the model, and so the relationship between $x$ and $y$ is biased. If we now factor in the influence of $group$, in some situations (e.g. the image above) this can lead us to completely reverse the sign of our estimate of $x \rightarrow y$, now estimating that increasing $x$ causes $y$ to _increase_. 
+
+In short, this 'paradox' (or simply ommitted variable bias) can be resolved by assuming a causal DAG which includes how the main predictor variable _and_ group membership (the confounding variable) influence the outcome variable. We demonstrate an example where we _don't_ incorporate group membership (so our causal DAG is wrong, or in other words our model is misspecified; Model 1). We then show 2 ways to resolve this by including group membership as causal influence upon $x$ and $y$. This is shown in an unpooled model (Model 2) and a hierarchical model (Model 3).
 
 ```{code-cell} ipython3
 import arviz as az
@@ -260,7 +262,7 @@ ax = az.plot_posterior(idata1.posterior["β1"], ref_val=0)
 ax.set(title="Model 1 strongly suggests a positive slope", xlabel=r"$\beta_1$");
 ```
 
-## Model 2: Unpooled regression
+## Model 2: Unpooled regression with counfounder included
 
 We will use the same data in this analysis, but this time we will use our knowledge that data come from groups. From a causal perspective we are exploring the notion that both $x$ and $y$ are influenced by group membership. This can be shown in the causal directed acyclic graph ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)) below.
 
@@ -272,10 +274,15 @@ g.node(name="x", label="x")
 g.node(name="g", label="group")
 g.node(name="y", label="y")
 g.edge(tail_name="x", head_name="y")
+g.edge(tail_name="g", head_name="x")
 g.edge(tail_name="g", head_name="y")
 g
 ```
 
+So we can see that $group$ is a [confounding variable](https://en.wikipedia.org/wiki/Confounding). So if we are trying to discover the causal relationship of $x$ on $y$, we need to account for the confounding variable $group$. Model 1 did not do this and so arrived at the wrong conclusion. But Model 2 will account for this.
+
++++
+
 More specifically we will essentially fit independent regressions to data within each group. This could also be described as an unpooled model. We could describe this model mathematically as:
 
 $$
@@ -425,7 +432,7 @@ ax[0].set(
 );
 ```
 
-## Model 3: Partial pooling model
+## Model 3: Partial pooling model with confounder included
 
 Model 3 assumes the same causal DAG as model 2 (see above). However, we can go further and incorporate more knowledge about the structure of our data. Rather than treating each group as entirely independent, we can use our knowledge that these groups are drawn from a population-level distribution. We could formalise this as saying that the group-level slopes and intercepts are modeled as deflections from a population-level slope and intercept, respectively.
 
@@ -471,7 +478,7 @@ We can also express this Model 3 in Wilkinson notation as `1 + x + (1 + x | g)`.
 
 * The `1` captures the global intercept, $\beta_0$.
 * The `x` captures the global slope, $\beta_1$.
-* The `(1 + x | g)` term captures group specific random effects for the intercept and slope.
+* The `(1 + x | g)` term captures group specific terms for the intercept and slope.
   * `1 | g` captures the group specific intercept deflections $\vec{u_0}$ parameters.
   * `x | g` captures the group specific slope deflections $\vec{u_1}[g_i]$ parameters.
 :::
@@ -643,16 +650,14 @@ This paradox is resolved by updating our causal DAG to include the group variabl
 
 Model 3 assumed the same causal DAG, but adds the knowledge that each of these groups are sampled from an overall population. This added the ability to make inferences not only about the regression parameters at the group level, but also at the population level.
 
-If you are interested in learning more, there are a number of other [PyMC examples](http://docs.pymc.io/nb_examples/index.html) covering hierarchical modelling and regression topics.
-
 +++
 
 ## Authors
 * Authored by [Benjamin T. Vincent](https://github.com/drbenvincent) in July 2021
 * Updated by [Benjamin T. Vincent](https://github.com/drbenvincent) in April 2022
 * Updated by [Benjamin T. Vincent](https://github.com/drbenvincent) in February 2023 to run on PyMC v5
 * Updated to use `az.extract` by [Benjamin T. Vincent](https://github.com/drbenvincent) in February 2023 ([pymc-examples#522](https://github.com/pymc-devs/pymc-examples/pull/522))
-* Updated by [Benjamin T. Vincent](https://github.com/drbenvincent) in September 2024 ([pymc-examples#697](https://github.com/pymc-devs/pymc-examples/pull/697))
+* Updated by [Benjamin T. Vincent](https://github.com/drbenvincent) in September 2024 ([pymc-examples#697](https://github.com/pymc-devs/pymc-examples/pull/697) and [pymc-examples#709](https://github.com/pymc-devs/pymc-examples/pull/709))
 
 +++