update paper with changes

paper/paper.md

@@ -12,7 +12,7 @@ authors:
   name: Nima S. Hejazi
   orcid: 0000-0002-7127-2789
 bibliography: paper.bib
-date: 08 January 2025
+date: 20 January 2025
 tags:
 - Julia
 - statistics
@@ -25,14 +25,14 @@ toc-title: Table of contents
 
 # Summary
 
-Estimating the strength of causal relationships between variables is an
-important problem across many scientific disciplines. `CausalTables.jl`
-provides tools to (1) easily store and process tabular data endowed with
-causal structure, (2) simulate data from causal models for experimental
-testing, and (3) compute ground-truth estimates of causal parameters.
-Together, these functionalities expand the Julia ecosystem by supporting
-the development and experimental assessment of new statistical causal
-inference methods in Julia.
+Estimating the strength of causal relationships between treatment and
+response variables is an important problem across many scientific
+disciplines. `CausalTables.jl` is a Julia package that helps
+statisticians and applied scientists create, manipulate, and simulate
+datasets labeled with relevant causal structure. Together, its
+functionalities expand the Julia ecosystem by supporting the development
+and experimental assessment of new statistical causal inference methods
+in Julia.
 
 # Statement of need
 
@@ -48,23 +48,23 @@ currently do not exist auxiliary tools designed to support their
 development. `CausalTables.jl` aims to provide such a tool.
 
 Attempts to implement and test causal inference methods in Julia face
-two major challenges. First, statistical causal inference requires data
-to be transformed in various ways based on the underlying causal
-structure, in order to be provided as input to other packages. For
-instance, causal methods using `MLJ.jl` [@blaom2020mlj] would
-necessitate extracting the variables hypothesized to cause the treatment
-and response as Tables; meanwhile; using `GLM.jl` [@bates2023glm] would
-require the same but as Matrix or `Formula` objects. Second, testing the
-performance of new estimators often requires simulating data for
-numerical experiments from a Structural Causal Model (SCM)
+two major challenges. First, statistical causal inference often requires
+extracting features from data based on their relationships with
+"treatment" and "response" variables; these might include confounders,
+mediators, or instruments. The format of these variables might even
+differ depending on downstream analysis package; for instance, `MLJ.jl`
+[@blaom2020mlj] requires input to be a Table, but `GLM.jl`
+[@bates2023glm] necessitates a Matrix or `Formula` object. Second,
+testing the performance of new estimators often requires simulating data
+for numerical experiments from a Structural Causal Model (SCM)
 [@pearl2009causality] so as to compare them to an underlying ground
 truth (encoded via interventions on the SCM).
 
 `CausalTables.jl` provides an interface to solve these two problems,
 simplifying the development of packages for statistical causal inference
 on tabular data in Julia. It implements a `CausalTable` interface that
-extends `Tables.jl`, the most common interface for accessing tabular
-data in Julia [@quinn2024tables]. The package also implements a
+extends `Tables.jl` [@quinn2024tables] to also store necessary causal
+relationships between variables. In addition, the package implements a
 `StructuralCausalModel` interface for sampling from any SCM and
 computing ground-truth estimates of causal parameters. This interface
 operates in conjunction with `Distributions.jl`, the primary Julia
@@ -111,7 +111,7 @@ tbl = (W = [0.2, 0.4, 0.7],
        Y = [0.8, 1.2, 2.3])
 
 # Wrap data as CausalTable
-ct_wrap = CausalTable(tbl; treatment = :A, response = :Y, confounders = [:W])
+ct_wrap = CausalTable(tbl; treatment = :A, response = :Y)
 ```
 :::
 
@@ -170,7 +170,7 @@ dgp = @dgp(
 
 # Create a structural causal model (SCM) from the DGP
 scm = StructuralCausalModel(dgp; 
-  treatment = :A, response = :Y, confounders = [:W]
+  treatment = :A, response = :Y
 )
 
 ct = rand(scm, 500) # randomly draw from the SCM
@@ -209,15 +209,15 @@ mean(conmean(scm, treated, :Y) .- conmean(scm, untreated, :Y))
 
 # Closing remarks
 
-`CausalTables.jl` provides useful auxiliary functions to support causal
-inference methods on tabular data in Julia that involve one or more
-treatment variables and responses. Users can simulate data from any SCM
-and benchmark methods using either high-level functions for common
-estimands or low-level functions for more exotic estimands. By combining
-this with the power of the `CausalTable` interface for processing data
-once it is generated, `CausalTables.jl` serves as a useful tool for
-scientists seeking to develop and experimentally evaluate new causal
-inference methods.
+Not only does `CausalTables.jl` provide high-level functions for common
+data processing and simulation tasks in causal inference, it can also be
+easily extended to support more novel methods and estimands using
+low-level functions. The `CausalTable` stores all relevant causal
+relationships needed to extract variables related to treatment and
+response variables. The `StructuralCausalModel` support simulating data
+from any SCM that can be expressed as a sequence of random variables.
+Hence, `CausalTables.jl` serves as a useful tool in Julia for both
+developing new methods and providing input to existing ones.
 
 # Acknowledgements
 

paper/paper.qmd

@@ -16,23 +16,23 @@ authors:
 affiliations:
  - name: Department of Biostatistics, Harvard T.H. Chan School of Public Health
    index: 1
-date: 08 January 2025
+date: 20 January 2025
 bibliography: paper.bib
 format: markdown
 engine: julia
 ---
 
 # Summary
 
-Estimating the strength of causal relationships between variables is an important problem across many scientific disciplines. `CausalTables.jl` provides tools to (1) easily store and process tabular data endowed with causal structure, (2) simulate data from causal models for experimental testing, and (3) compute ground-truth estimates of causal parameters. Together, these functionalities expand the Julia ecosystem by supporting the development and experimental assessment of new statistical causal inference methods in Julia. 
+Estimating the strength of causal relationships between treatment and response variables is an important problem across many scientific disciplines. `CausalTables.jl` is a Julia package that helps statisticians and applied scientists create, manipulate, and simulate datasets labeled with relevant causal structure. Together, its functionalities expand the Julia ecosystem by supporting the development and experimental assessment of new statistical causal inference methods in Julia.
 
 # Statement of need
 
 The quantitative science of causal inference has emerged over the past three decades as a set of formalisms for studying cause-and-effect relationships between variables from observed data [@pearl2009causality; @hernan2020causal]. Causal inference techniques have helped applied scientists and decision-makers better understand important phenomena in fields ranging from health and medicine to politics and economics. New software tools for causal inference are being developed at a rapid pace, but in the Julia language, there currently do not exist auxiliary tools designed to support their development. `CausalTables.jl` aims to provide such a tool. 
 
-Attempts to implement and test causal inference methods in Julia face two major challenges. First, statistical causal inference requires data to be transformed in various ways based on the underlying causal structure, in order to be provided as input to other packages. For instance, causal methods using `MLJ.jl` [@blaom2020mlj] would necessitate extracting the variables hypothesized to cause the treatment and response as Tables; meanwhile; using `GLM.jl` [@bates2023glm] would require the same but as Matrix or `Formula` objects. Second, testing the performance of new estimators often requires simulating data for numerical experiments from a Structural Causal Model (SCM) [@pearl2009causality] so as to compare them to an underlying ground truth (encoded via interventions on the SCM). 
+Attempts to implement and test causal inference methods in Julia face two major challenges. First, statistical causal inference often requires extracting features from data based on their relationships with "treatment" and "response" variables; these might include confounders, mediators, or instruments. The format of these variables might even differ depending on downstream analysis package; for instance, `MLJ.jl` [@blaom2020mlj] requires input to be a Table, but `GLM.jl` [@bates2023glm] necessitates a Matrix or `Formula` object. Second, testing the performance of new estimators often requires simulating data for numerical experiments from a Structural Causal Model (SCM) [@pearl2009causality] so as to compare them to an underlying ground truth (encoded via interventions on the SCM). 
 
-`CausalTables.jl` provides an interface to solve these two problems, simplifying the development of packages for statistical causal inference on tabular data in Julia. It implements a `CausalTable` interface that extends `Tables.jl`, the most common interface for accessing tabular data in Julia [@quinn2024tables]. The package also implements a `StructuralCausalModel` interface for sampling from any SCM and computing ground-truth estimates of causal parameters. This interface operates in conjunction with `Distributions.jl`, the primary Julia package for working with random variables [@JSSv098i16; @Distributions.jl-2019]. By integrating seamlessly with other commonly used packages in the Julia ecosystem, `CausalTables.jl` ensures both compatibility and ease of use for statisticians and applied scientists alike. 
+`CausalTables.jl` provides an interface to solve these two problems, simplifying the development of packages for statistical causal inference on tabular data in Julia. It implements a `CausalTable` interface that extends `Tables.jl` [@quinn2024tables] to also store necessary causal relationships between variables. In addition, the package implements a `StructuralCausalModel` interface for sampling from any SCM and computing ground-truth estimates of causal parameters. This interface operates in conjunction with `Distributions.jl`, the primary Julia package for working with random variables [@JSSv098i16; @Distributions.jl-2019]. By integrating seamlessly with other commonly used packages in the Julia ecosystem, `CausalTables.jl` ensures both compatibility and ease of use for statisticians and applied scientists alike. 
 
 # Comparison to existing packages
 
@@ -61,7 +61,7 @@ tbl = (W = [0.2, 0.4, 0.7],
        Y = [0.8, 1.2, 2.3])
 
 # Wrap data as CausalTable
-ct_wrap = CausalTable(tbl; treatment = :A, response = :Y, confounders = [:W])
+ct_wrap = CausalTable(tbl; treatment = :A, response = :Y)
 ```
 
 Convenience functions perform data processing tasks common to causal inference, such as selecting or intervening on specific variables. For example, the `parents` function can be used to select only variables denoted as causes of $Y$:
@@ -95,7 +95,7 @@ dgp = @dgp(
 
 # Create a structural causal model (SCM) from the DGP
 scm = StructuralCausalModel(dgp; 
-  treatment = :A, response = :Y, confounders = [:W]
+  treatment = :A, response = :Y
 )
 
 ct = rand(scm, 500) # randomly draw from the SCM
@@ -117,7 +117,7 @@ mean(conmean(scm, treated, :Y) .- conmean(scm, untreated, :Y))
 
 # Closing remarks
 
-`CausalTables.jl` provides useful auxiliary functions to support causal inference methods on tabular data in Julia that involve one or more treatment variables and responses. Users can simulate data from any SCM and benchmark methods using either high-level functions for common estimands or low-level functions for more exotic estimands. By combining this with the power of the `CausalTable` interface for processing data once it is generated, `CausalTables.jl` serves as a useful tool for scientists seeking to develop and experimentally evaluate new causal inference methods.
+Not only does `CausalTables.jl` provide high-level functions for common data processing and simulation tasks in causal inference, it can also be easily extended to support more novel methods and estimands using low-level functions. The `CausalTable` stores all relevant causal relationships needed to extract variables related to treatment and response variables. The `StructuralCausalModel` support simulating data from any SCM that can be expressed as a sequence of random variables. Hence, `CausalTables.jl` serves as a useful tool in Julia for both developing new methods and providing input to existing ones. 
 
 # Acknowledgements