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diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![JOSS Status](https://joss.theoj.org/papers/68c43e832d063050a4e67528191e8148/status.svg)](https://joss.theoj.org/papers/68c43e832d063050a4e67528191e8148)
 
-*A common interface for storing and simulating data for causal inference in Julia.*
+*A common interface for processing and simulating data for causal inference in Julia.*
 
 [Causal inference](https://en.wikipedia.org/wiki/Causal_inference) is the process of estimating, from data, the effect of a treatment variable on an outcome variable -- typically in the presence of confounders. The goal of `CausalTables.jl` is to simplify the development of statistical causal inference methods in Julia. To this end, the package provides two sets of tools:
 

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,6 +1,6 @@
 # CausalTables.jl
 
-*A common interface for storing and simulating data for causal inference in Julia.*
+*A common interface for processing and simulating data for causal inference in Julia.*
 
 ## Overview
 

diff --git a/docs/src/man/formatting.md b/docs/src/man/formatting.md
@@ -51,7 +51,7 @@ In addition, the `CausalTable` object has several utility functions that can be
 # Additional utility functions for CausalTables
 treatment(ctbl)              # get CausalTable of treatment variables
 response(ctbl)               # get CausalTable of response variables
-treatmentparents(ctbl)      # get CausalTable of treatment and response
+treatmentparents(ctbl)       # get CausalTable of treatment and response
 responseparents(ctbl)        # get CausalTable of treatment and confounders
 
 parents(ctbl, :NOX)          # get CausalTable of parents of a particular variable

diff --git a/docs/src/man/generating-data.md b/docs/src/man/generating-data.md
@@ -128,9 +128,6 @@ In the above, the keys of `causes` denote the variables whose causes are being s
 
     `causes` must be specified manually unless the user is assuming that all unlabeled variables cause both `treatment` and `outcome`. This is the default assumption of a `StructuralCausalModel`, but it may not not factually match the model encoded by the `DataGeneratingProcess`. This behavior is allowed for two reasons: (1) to permit a random draw of a `CausalTable` with an 'incorrect' causal model, which can be useful for benchmarking the robustness of different causal inference methods to model misspecification, and (2) to simulate causal models that implicitly condition on a particular set of variables by leaving them out of the `causes` argument. Otherwise, ensure that labels in `causes` do not contradict the data generating process! 
 
-Finally, in some cases it may be convenient to define intermediate variables within a DGP
-
-
 ## Networks of Causally-Connected Units
 
 In some cases, we might work with data in which units may *not* be causally independent, but rather, in which one unit's variables could dependent on some summary function of its neighbors. Generating data from such a model can be done by adding lines of the form `Xs $ NetworkSummary` to the `@dgp` macro.

diff --git a/docs/src/man/ground-truth.md b/docs/src/man/ground-truth.md
@@ -52,7 +52,6 @@ To evaluate the causal effects of continuous treatments, one can apply the `addi
 Once we've defined an SCM (see [Generating data for statistical experiments](generating-data.md)) and have some table of intervened data with variables matching those of our DGP, we can compute the ground truth conditional distributions of any variable (given a corresponding DGP) using the `condensity` function. For any line starting with a `~` in the DGP, `condensity` will be able to return the true conditional distribution, a Distribution object from the package [Distributions.jl](https://juliastats.org/Distributions.jl/stable/), given the data. Some examples are shown below:
 
 ```jldoctest truthtest; output = false, filter = r"(?<=.{18}).*"s
-
 # Distribution of the treatment in the observed data
 A_distribution = condensity(scm, ct, :A)
 

diff --git a/paper/paper.md b/paper/paper.md
@@ -12,13 +12,13 @@ authors:
   name: Nima S. Hejazi
   orcid: 0000-0002-7127-2789
 bibliography: paper.bib
-date: 6 February 2025
+date: 13 February 2025
 tags:
 - Julia
 - statistics
 - causal inference
 - tables
-title: "CausalTables.jl: Simulating and storing data for statistical
+title: "CausalTables.jl: Simulating and processing data for statistical
   causal inference in Julia"
 toc-title: Table of contents
 ---
@@ -148,7 +148,7 @@ with each draw depending on the draws preceding it. For example:
 
 `\begin{align*}
 W &\sim Beta(2, 4) \\
-A &\sim Bernoulli(W) \\
+A &\sim Bernoulli(0.5W + 0.2) \\
 Y &\sim Normal(A + W, 1)
 \end{align*}`{=tex}
 
@@ -181,28 +181,12 @@ For instance, computing an ATE compares hypothetical responses had
 everyone been treated versus no one treated; one can apply these
 interventions on a `CausalTable` using the `intervene` function:
 
-:::: {.cell execution_count="1"}
+::: {.cell execution_count="1"}
 ``` {.julia .cell-code}
 treated = intervene(ct, treat_all)
 untreated = intervene(ct, treat_none)
 ```
-
-::: {.cell-output .cell-output-display execution_count="1"}
-    CausalTable
-    ┌─────────┬─────────┬─────────┐
-    │       W │       A │       Y │
-    │ Float64 │ Float64 │ Float64 │
-    ├─────────┼─────────┼─────────┤
-    │     0.4 │     0.0 │     2.0 │
-    │     0.3 │     0.0 │     1.1 │
-    │     0.2 │     0.0 │     3.3 │
-    │     0.2 │     0.0 │    -1.3 │
-    │     0.4 │     0.0 │    -1.0 │
-    └─────────┴─────────┴─────────┘
-    Summaries: NamedTuple()
-    Arrays: NamedTuple()
 :::
-::::
 
 After simulating data, the true ("oracle") distribution can be obtained
 using `condensity`. Other functions obtain specific features, such as

diff --git a/paper/paper.qmd b/paper/paper.qmd
@@ -1,5 +1,5 @@
 ---
-title: "CausalTables.jl: Simulating and storing data for statistical causal inference in Julia"
+title: "CausalTables.jl: Simulating and processing data for statistical causal inference in Julia"
 tags:
   - Julia
   - statistics
@@ -16,7 +16,7 @@ authors:
 affiliations:
  - name: Department of Biostatistics, Harvard T.H. Chan School of Public Health
    index: 1
-date: 6 February 2025
+date: 13 February 2025
 bibliography: paper.bib
 format: markdown
 engine: julia
@@ -76,7 +76,7 @@ An SCM defines causal structure by envisaging a data-generating process as rando
 
 \begin{align*}
 W &\sim Beta(2, 4) \\
-A &\sim Bernoulli(W) \\
+A &\sim Bernoulli(0.5W + 0.2) \\
 Y &\sim Normal(A + W, 1)
 \end{align*}
 
@@ -104,6 +104,7 @@ ct = rand(scm, 5) # randomly sample
 Many causal estimands involve applying some intervention to a treatment. For instance, computing an ATE compares hypothetical responses had everyone been treated versus no one treated; one can apply these interventions on a `CausalTable` using the `intervene` function:
 
 ```{julia}
+#| output: false
 treated = intervene(ct, treat_all)
 untreated = intervene(ct, treat_none)
 ```