Merge branch 'jmafoster1/remove-data-collector' of github.com:CITCOM-project/CausalTestingFramework into jmafoster1/remove-data-collector

Author: jmafoster1

.github/workflows/ci-tests-drafts.yaml

@@ -30,8 +30,7 @@ jobs:
       run: |
         pytest --cov=causal_testing --cov-report=xml
     - name: "Upload coverage to Codecov"
-      uses: codecov/codecov-action@v2
+      uses: codecov/codecov-action@v5
       with:
         fail_ci_if_error: true
         token: ${{ secrets.CODECOV_TOKEN }}
-        version: "v0.1.15"

.github/workflows/ci-tests.yaml

@@ -36,8 +36,7 @@ jobs:
       run: |
         pytest --cov=causal_testing --cov-report=xml
     - name: "Upload coverage to Codecov"
-      uses: codecov/codecov-action@v2
+      uses: codecov/codecov-action@v5
       with:
         fail_ci_if_error: true
         token: ${{ secrets.CODECOV_TOKEN }}
-        version: "v0.1.15"

paper/paper.bib

@@ -114,6 +114,6 @@ @article{textor2017dagitty
   month = {jan},
   pages = {dyw341},
   publisher = {Oxford University Press (OUP)},
-  title = {Robust causal inference using directed acyclic graphs: the R package ‘dagitty’},
+  title = {Robust causal inference using directed acyclic graphs: the {R} package ‘dagitty’},
   year = {2017}
 }

paper/paper.md

@@ -45,7 +45,7 @@ bibliography: paper.bib
 
 # Summary
 Scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical.
-In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal Inference (CI) [@pearl2009causality,@hernan2020causal] has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse observational data instead of costly randomised trials.
+In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal Inference (CI) [@pearl2009causality; @hernan2020causal] has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse observational data instead of costly randomised trials.
 CI works by using domain knowledge to identify and mitigate for biases in the data, enabling them to answer causal questions that concern the effect of changing some feature on the observed outcome.
 The Causal Testing Framework (CTF) is a software testing framework that uses CI techniques to establish causal effects between software variables from pre-existing runtime data rather than having to collect bespoke, highly curated datasets especially for testing.
 
@@ -56,7 +56,7 @@ Nondeterministic software can be tested using Statistical Metamorphic Testing [@
 However, this requires the software to be executed repeatedly for each set of parameters of interest, so is computationally expensive, and is constrained to testing properties over software inputs that can be directly and precisely controlled.
 Statistical Metamorphic Testing cannot be used to test properties that relate internal variables or outputs to each other, since these cannot be controlled a priori.
 
-By employing domain knowledge in the form of a causal graph --- a lightweight model specifying the expected relationships between key software variables --- the CTF overcomes the limitations of Statistical Metamorphic Testing by enabling models to be tested using pre-existing runtime data.
+By employing domain knowledge in the form of a causal graph---a lightweight model specifying the expected relationships between key software variables---the CTF overcomes the limitations of Statistical Metamorphic Testing by enabling models to be tested using pre-existing runtime data.
 The CTF is written in Python but is language agnostic in terms of the system under test.
 All that is required is a set of properties to be validated, a causal model, and a set of software runtime data.