diff --git a/.github/workflows/publish-to-dafni.yaml b/.github/workflows/publish-to-dafni.yaml
index 424a0fe6..051655de 100644
--- a/.github/workflows/publish-to-dafni.yaml
+++ b/.github/workflows/publish-to-dafni.yaml
@@ -18,8 +18,9 @@ jobs:
       - name: Get release version
         id: get_release
         run: |
-          export VERSION=$(curl -sSL https://api.github.com/repos/CITCOM-project/CausalTestingFramework/releases/latest | jq -r '.tag_name' | head -c 2)
-          echo "::set-output name=version::$VERSION"
+          # Extract the version without the 'v' prefix using regex
+          export VERSION=$(curl -sSL https://api.github.com/repos/CITCOM-project/CausalTestingFramework/releases/latest | jq -r '.tag_name' | sed -E 's/^v?([0-9]+(\.[0-9]+)?).*/\1/')
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
 
   build_and_upload:
     name: Docker Build
@@ -38,17 +39,28 @@ jobs:
         with:
           python-version: '3.10'
 
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install causal-testing-framework -e .[dev]
+          python -m pip install dafni-cli
+
+      - name: Check if unit tests are passing first
+        id: tests
+        uses: ./.github/workflows/ci-tests.yml
+
       - name: Build the container
+        if: success() && steps.tests.outcome == 'success'
         run: |
           docker build -t ctf:${{ env.VERSION }} -f ./dafni/Dockerfile .
           docker save ctf:${{ env.VERSION }} | gzip > ctf-dafni-${{ env.VERSION }}.tar.gz
 
-      - name: Install DAFNI-CLI and log in
+
+      - name: Log into DAFNI
         run: |
-          python -m pip install dafni-cli
           dafni login
 
       - name: Upload to DAFNI
         run: |
-          dafni upload model ./dafni/model_definition.yaml ctf-dafni-${{ env.VERSION }}.tar.gz --version-message "Causal Testing Framework ${{ env.VERSION }}. Uploaded via Github Actions." --parent-id ${{ env.DAFNI_PARENT_ID }} -y
-          dafni logout
+          dafni upload model ./dafni/model_definition.yaml ctf-dafni-${{ env.VERSION }}.tar.gz --version-message "Causal Testing Framework v${{ env.VERSION }}. Uploaded via Github Actions." --parent-id ${{ env.DAFNI_PARENT_ID }} -y
+          dafni logout
\ No newline at end of file
diff --git a/dafni/Dockerfile b/dafni/Dockerfile
index 8969ad3f..2330a9df 100644
--- a/dafni/Dockerfile
+++ b/dafni/Dockerfile
@@ -1,5 +1,5 @@
 # Define the Python version neded for CTF
-FROM python:3.10-slim
+FROM python:3.12-slim
 
 ## Prevents Python from writing pyc files
 ENV PYTHONDONTWRITEBYTECODE=1
@@ -8,26 +8,22 @@ ENV PYTHONDONTWRITEBYTECODE=1
 ## from crashing without emitting any logs due to buffering
 ENV PYTHONUNBUFFERED=1
 
-#Label maintainer
+# Label maintainer
 LABEL maintainer="Dr. Farhad Allian - The University of Sheffield"
 
 # Create a folder for the source code/outputs
 RUN mkdir -p ./causal_testing
 RUN mkdir -p ./data/outputs
 
-# Copy the source code and test files from build into the container
-COPY --chown=nobody  ../causal_testing ./causal_testing
-COPY --chown=nobody ./dafni/main_dafni.py ./
+# Copy the source code from local root and test files from build into the container
+COPY --chown=nobody  ./causal_testing ./causal_testing
 COPY --chown=nobody ./dafni/data/inputs ./data/inputs
 
 # Install core dependencies using PyPi
 RUN pip install causal-testing-framework --no-cache-dir
 
-#For local testing purposes
-ENV VARIABLES_PATH=./data/inputs/variables.json \
-    CAUSAL_TESTS=./data/inputs/causal_tests.json \
-    DATA_PATH=./data/inputs/runtime_data.csv \
-    DAG_PATH=./data/inputs/dag.dot
+# Set the PYTHONPATH environment variable to include the /src directory
+ENV PYTHONPATH="/causal_testing:${PYTHONPATH}"
 
 # Define the entrypoint/commands
-CMD python main_dafni.py --variables_path $VARIABLES_PATH --dag_path $DAG_PATH --data_path $DATA_PATH --tests_path $CAUSAL_TESTS
+CMD python -m causal_testing --dag_path $DAG_PATH --data_path $DATA_PATH --test_config $TESTS_PATH --output $OUTPUT
\ No newline at end of file
diff --git a/dafni/README.md b/dafni/README.md
index 29833d61..47f0b186 100644
--- a/dafni/README.md
+++ b/dafni/README.md
@@ -5,25 +5,29 @@ to upload the framework onto [DAFNI](https://www.dafni.ac.uk).
 - It is **not** recommended to install the causal testing framework using Docker, and should only be installed
   using [PyPI](https://pypi.org/project/causal-testing-framework/).
 
-### Folders
+### Directory Hierarchy
 
 - `data` contains two sub-folders (the structure is important for DAFNI).
   - `inputs` is a folder that contains the input files that are (separately) uploaded to DAFNI.
     - `causal_tests.json` is a JSON file that contains the causal tests.
-    - `variables.json` is a JSON file that contains the variables and constraints to be used.
-    - `dag.dot` is a dot file that contains the directed acyclc graph (dag) file.
-    - `runtime_data.csv` is a csv file that contains the runtime data.
+    - `dag.dot` is a dot file that contains the directed acyclic graph (dag). In this file, Causal Variables are defined as 
+       node metadata attributes as key-value pairs using the following syntax: 
+       `node [datatype="int", typestring="input"]`. The `datatype` key specifies the datatype of the causal variable
+       as a string (e.g. `"int"`, `"str"`) and the `typestring` key specifies its typestring, which is also a string 
+       representing the variable type (e.g. `"input"` or `"output"`).
+    - `runtime_data.csv` is the `.csv` file that contains the runtime data.
 
   - `outputs` is a folder where the `causal_tests_results.json` output file is created.
 
 ### Docker files
-- `main_dafni.py` is the entry-point to the causal testing framework that is used by Docker.
 - `model_definition.yaml` is the model metadata that is required to be uploaded to DAFNI.
-- `.env` is an example of a configuration file containing the environment variables. This is only required
-    if using `docker-compose` to build the image. 
-- `Dockerfile` is the main blueprint that builds the image.
-- `.dockerignore` tells the Dockerfile which files to not include in the image.
+- `Dockerfile` is the main blueprint that builds the image. The main command calls the `causal_testing` module, 
+   with specified paths for the DAG, input runtime data, test configurations, and the output filename as defined above. 
+   This command is identical to that referenced in the main [README.md](../README.md) file.
 - `docker-compose.yaml` is another method of building the image and running the container in one line. 
    Note: the `.env` file that contains the environment variables for `main_dafni.py` is only used here.
+- `.dockerignore` tells the Dockerfile which files to not include in the image.
+- `.env` is an example of a configuration file containing the environment variables. This is only required
+    if using `docker-compose` to build the image. 
 
 
diff --git a/dafni/data/inputs/causal_tests.json b/dafni/data/inputs/causal_tests.json
index 6c78c57f..539dd927 100644
--- a/dafni/data/inputs/causal_tests.json
+++ b/dafni/data/inputs/causal_tests.json
@@ -1,58 +1,11 @@
 {
   "tests": [
-    {
-      "name": "max_doses _||_ cum_vaccinations",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_vaccinations": "NoEffect"
-      },
-      "formula": "cum_vaccinations ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
-    {
-      "name": "max_doses _||_ cum_vaccinated",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_vaccinated": "NoEffect"
-      },
-      "formula": "cum_vaccinated ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
-    {
-      "name": "max_doses _||_ cum_infections",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_infections": "NoEffect"
-      },
-      "formula": "cum_infections ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
     {
       "name": "vaccine --> cum_vaccinations",
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_vaccinations": "SomeEffect"
       },
@@ -64,9 +17,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_vaccinated": "SomeEffect"
       },
@@ -78,9 +29,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_infections": "SomeEffect"
       },
@@ -92,9 +41,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinations"
-      ],
+      "treatment_variable": "cum_vaccinations",
       "expected_effect": {
         "cum_vaccinated": "NoEffect"
       },
@@ -107,9 +54,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinations"
-      ],
+      "treatment_variable": "cum_vaccinations",
       "expected_effect": {
         "cum_infections": "NoEffect"
       },
@@ -122,9 +67,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinated"
-      ],
+      "treatment_variable": "cum_vaccinated",
       "expected_effect": {
         "cum_infections": "NoEffect"
       },
diff --git a/dafni/data/inputs/dag.dot b/dafni/data/inputs/dag.dot
index 43628817..67b9007b 100644
--- a/dafni/data/inputs/dag.dot
+++ b/dafni/data/inputs/dag.dot
@@ -1,7 +1,12 @@
 digraph CausalDAG {
     rankdir=LR;
-    "vaccine" -> "cum_vaccinations";
-    "vaccine" -> "cum_vaccinated";
-    "vaccine" -> "cum_infections";
-    "max_doses";
+    vaccine [datatype="int", typestring="input"];
+    cum_vaccinations [datatype="int", typestring="output"];
+    cum_vaccinated [datatype="int", typestring="output"];
+    cum_infections [datatype="int", typestring="output"];
+    max_doses [datatype="int", typestring="output"];
+    vaccine -> cum_vaccinations;
+    vaccine -> cum_vaccinated;
+    vaccine -> cum_infections;
+    max_doses;
 }
\ No newline at end of file
diff --git a/dafni/data/inputs/variables.json b/dafni/data/inputs/variables.json
deleted file mode 100644
index 8e4a1add..00000000
--- a/dafni/data/inputs/variables.json
+++ /dev/null
@@ -1,47 +0,0 @@
-{
-  "variables": [
-    {
-      "name": "pop_size",
-      "datatype": "int",
-      "typestring": "Input",
-      "constraint": 50000
-    },
-    {
-      "name": "pop_infected",
-      "datatype": "int",
-      "typestring": "Input",
-      "constraint": 1000
-    },
-    {
-      "name": "n_days",
-      "datatype": "int",
-      "typestring": "Input",
-      "constraint": 50
-    },
-    {
-      "name": "vaccine",
-      "datatype": "int",
-      "typestring": "Input"
-    },
-    {
-      "name": "cum_infections",
-      "datatype": "int",
-      "typestring": "Output"
-    },
-    {
-      "name": "cum_vaccinations",
-      "datatype": "int",
-      "typestring": "Output"
-    },
-    {
-      "name": "cum_vaccinated",
-      "datatype": "int",
-      "typestring": "Output"
-    },
-    {
-      "name": "max_doses",
-      "datatype": "int",
-      "typestring": "Output"
-    }
-  ]
-}
\ No newline at end of file
diff --git a/dafni/data/outputs/causal_tests_results.json b/dafni/data/outputs/causal_tests_results.json
index f5e503aa..00dcbc6c 100644
--- a/dafni/data/outputs/causal_tests_results.json
+++ b/dafni/data/outputs/causal_tests_results.json
@@ -1,215 +1,176 @@
 [
-  {
-    "name": "max_doses _||_ cum_vaccinations",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "mutations": [
-      "max_doses"
-    ],
-    "expected_effect": {
-      "cum_vaccinations": "NoEffect"
-    },
-    "formula": "cum_vaccinations ~ max_doses",
-    "alpha": 0.05,
-    "skip": false,
-    "failed": true,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_vaccinations",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": 252628.1066666667,
-      "ci_low": 252271.33332001517,
-      "ci_high": 252984.8800133182
-    }
-  },
-  {
-    "name": "max_doses _||_ cum_vaccinated",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "mutations": [
-      "max_doses"
-    ],
-    "expected_effect": {
-      "cum_vaccinated": "NoEffect"
-    },
-    "formula": "cum_vaccinated ~ max_doses",
-    "alpha": 0.05,
-    "skip": false,
-    "failed": true,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_vaccinated",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": 213111.93333333335,
-      "ci_low": 212755.15056812647,
-      "ci_high": 213468.71609854023
-    }
-  },
-  {
-    "name": "max_doses _||_ cum_infections",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "mutations": [
-      "max_doses"
-    ],
-    "expected_effect": {
-      "cum_infections": "NoEffect"
-    },
-    "formula": "cum_infections ~ max_doses",
-    "alpha": 0.05,
-    "skip": false,
-    "failed": true,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_infections",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": 2666.3066666666664,
-      "ci_low": 2619.972040648758,
-      "ci_high": 2712.6412926845746
-    }
-  },
   {
     "name": "vaccine --> cum_vaccinations",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "vaccine"
-    ],
+    "treatment_variable": "vaccine",
     "expected_effect": {
       "cum_vaccinations": "SomeEffect"
     },
     "formula": "cum_vaccinations ~ vaccine",
+    "alpha": 0.05,
     "skip": false,
-    "failed": false,
+    "passed": true,
     "result": {
       "treatment": "vaccine",
       "outcome": "cum_vaccinations",
       "adjustment_set": [],
       "effect_measure": "coefficient",
-      "effect_estimate": 315785.1333333332,
-      "ci_low": 315339.1666500188,
-      "ci_high": 316231.1000166476
+      "effect_estimate": {
+        "vaccine": 315785.1333333332
+      },
+      "ci_low": [
+        315339.1666500188
+      ],
+      "ci_high": [
+        316231.1000166476
+      ]
     }
   },
   {
     "name": "vaccine --> cum_vaccinated",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "vaccine"
-    ],
+    "treatment_variable": "vaccine",
     "expected_effect": {
       "cum_vaccinated": "SomeEffect"
     },
     "formula": "cum_vaccinated ~ vaccine",
+    "alpha": 0.05,
     "skip": false,
-    "failed": false,
+    "passed": true,
     "result": {
       "treatment": "vaccine",
       "outcome": "cum_vaccinated",
       "adjustment_set": [],
       "effect_measure": "coefficient",
-      "effect_estimate": 266389.91666666657,
-      "ci_low": 265943.93821015797,
-      "ci_high": 266835.89512317517
+      "effect_estimate": {
+        "vaccine": 266389.91666666657
+      },
+      "ci_low": [
+        265943.93821015797
+      ],
+      "ci_high": [
+        266835.89512317517
+      ]
     }
   },
   {
     "name": "vaccine --> cum_infections",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "vaccine"
-    ],
+    "treatment_variable": "vaccine",
     "expected_effect": {
       "cum_infections": "SomeEffect"
     },
     "formula": "cum_infections ~ vaccine",
+    "alpha": 0.05,
     "skip": false,
-    "failed": false,
+    "passed": true,
     "result": {
       "treatment": "vaccine",
       "outcome": "cum_infections",
       "adjustment_set": [],
       "effect_measure": "coefficient",
-      "effect_estimate": 3332.883333333332,
-      "ci_low": 3274.9650508109467,
-      "ci_high": 3390.801615855717
+      "effect_estimate": {
+        "vaccine": 3332.883333333332
+      },
+      "ci_low": [
+        3274.9650508109467
+      ],
+      "ci_high": [
+        3390.801615855717
+      ]
     }
   },
   {
     "name": "cum_vaccinations _||_ cum_vaccinated | ['vaccine']",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "cum_vaccinations"
-    ],
+    "treatment_variable": "cum_vaccinations",
     "expected_effect": {
       "cum_vaccinated": "NoEffect"
     },
     "formula": "cum_vaccinated ~ cum_vaccinations + vaccine",
     "alpha": 0.05,
     "skip": false,
-    "failed": true,
+    "passed": false,
     "result": {
       "treatment": "cum_vaccinations",
       "outcome": "cum_vaccinated",
-      "adjustment_set": [],
+      "adjustment_set": [
+        "vaccine"
+      ],
       "effect_measure": "coefficient",
-      "effect_estimate": 0.9998656401531605,
-      "ci_low": 0.9929245394499968,
-      "ci_high": 1.0068067408563242
+      "effect_estimate": {
+        "cum_vaccinations": 0.9998656401531605
+      },
+      "ci_low": [
+        0.9929245394499968
+      ],
+      "ci_high": [
+        1.0068067408563242
+      ]
     }
   },
   {
     "name": "cum_vaccinations _||_ cum_infections | ['vaccine']",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "cum_vaccinations"
-    ],
+    "treatment_variable": "cum_vaccinations",
     "expected_effect": {
       "cum_infections": "NoEffect"
     },
     "formula": "cum_infections ~ cum_vaccinations + vaccine",
     "alpha": 0.05,
     "skip": false,
-    "failed": false,
+    "passed": true,
     "result": {
       "treatment": "cum_vaccinations",
       "outcome": "cum_infections",
-      "adjustment_set": [],
+      "adjustment_set": [
+        "vaccine"
+      ],
       "effect_measure": "coefficient",
-      "effect_estimate": -0.006416682407515084,
-      "ci_low": -0.05663010083886572,
-      "ci_high": 0.043796736023835554
+      "effect_estimate": {
+        "cum_vaccinations": -0.006416682407515084
+      },
+      "ci_low": [
+        -0.05663010083886572
+      ],
+      "ci_high": [
+        0.043796736023835554
+      ]
     }
   },
   {
     "name": "cum_vaccinated _||_ cum_infections | ['vaccine']",
     "estimate_type": "coefficient",
     "effect": "direct",
-    "mutations": [
-      "cum_vaccinated"
-    ],
+    "treatment_variable": "cum_vaccinated",
     "expected_effect": {
       "cum_infections": "NoEffect"
     },
     "formula": "cum_infections ~ cum_vaccinated + vaccine",
     "alpha": 0.05,
     "skip": false,
-    "failed": false,
+    "passed": true,
     "result": {
       "treatment": "cum_vaccinated",
       "outcome": "cum_infections",
-      "adjustment_set": [],
+      "adjustment_set": [
+        "vaccine"
+      ],
       "effect_measure": "coefficient",
-      "effect_estimate": -0.006176900588291234,
-      "ci_low": -0.05639349612119588,
-      "ci_high": 0.04403969494461341
+      "effect_estimate": {
+        "cum_vaccinated": -0.006176900588291234
+      },
+      "ci_low": [
+        -0.05639349612119588
+      ],
+      "ci_high": [
+        0.04403969494461341
+      ]
     }
   }
-]
+]
\ No newline at end of file
diff --git a/dafni/docker-compose.yaml b/dafni/docker-compose.yaml
index 9a38d1c4..bc18197b 100644
--- a/dafni/docker-compose.yaml
+++ b/dafni/docker-compose.yaml
@@ -1,10 +1,9 @@
-version: '3'
 services:
   causal-testing-framework:
     build:
       context: ../
-      dockerfile: ./dafni/Dockerfile
+      dockerfile: dafni/Dockerfile
     env_file:
       - .env
     volumes:
-      - .:/usr/src/app
+      - .:/usr/src/
diff --git a/dafni/main_dafni.py b/dafni/main_dafni.py
index a3d8f5c6..e69de29b 100644
--- a/dafni/main_dafni.py
+++ b/dafni/main_dafni.py
@@ -1,211 +0,0 @@
-"""
-
-Entrypoint script to run the causal testing framework on DAFNI
-
-"""
-
-from pathlib import Path
-import argparse
-import json
-import pandas as pd
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
-from causal_testing.testing.causal_effect import Positive, Negative, NoEffect, SomeEffect
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.estimation.logistic_regression_estimator import LogisticRegressionEstimator
-from causal_testing.json_front.json_class import JsonUtility
-
-
-class ValidationError(Exception):
-    """
-    Custom class to capture validation errors in this script
-    """
-
-
-def get_args(test_args=None) -> argparse.Namespace:
-    """
-    Function to parse arguments from the user using the CLI
-    :param test_args: None
-    :returns:
-            - argparse.Namespace - A Namsespace consisting of the arguments to this script
-    """
-    parser = argparse.ArgumentParser(description="A script for running the CTF on DAFNI.")
-    parser.add_argument("-d", "--data_path", required=True, help="Path to the input runtime data (.csv)", nargs="+")
-    parser.add_argument(
-        "-t", "--tests_path", required=True, help="Input configuration file path " "containing the causal tests (.json)"
-    )
-    parser.add_argument(
-        "-v",
-        "--variables_path",
-        required=True,
-        help="Input configuration file path " "containing the predefined variables (.json)",
-    )
-    parser.add_argument(
-        "-D",
-        "--dag_path",
-        required=True,
-        help="Input configuration file path containing a valid DAG (.dot). "
-        "Note: this must be supplied if the --tests argument isn't provided.",
-    )
-    parser.add_argument(
-        "-i", "--ignore_cycles", action="store_true", help="Whether to ignore cycles in the DAG.", default=False
-    )
-    parser.add_argument(
-        "-f", default=False, help="(Optional) Failure flag to step the framework from running if a test has failed."
-    )
-    parser.add_argument("-o", "--output_path", required=False, help="Path to the output directory.")
-    parser.add_argument(
-        "-w",
-        default=False,
-        help="(Optional) Specify to overwrite any existing output files. "
-        "This can lead to the loss of existing outputs if not careful",
-    )
-    args = parser.parse_args(test_args)
-
-    # Convert these to Path objects for main()
-
-    args.variables_path = Path(args.variables_path)
-
-    args.tests_path = Path(args.tests_path)
-
-    if args.dag_path is not None:
-        args.dag_path = Path(args.dag_path)
-
-    if args.output_path is None:
-        args.output_path = "./data/outputs/causal_tests_results.json"
-
-        Path(args.output_path).parent.mkdir(exist_ok=True)
-
-    else:
-        args.output_path = Path(args.output_path)
-
-        args.output_path.parent.mkdir(exist_ok=True)
-
-    return args
-
-
-def read_variables(variables_path: Path) -> FileNotFoundError | dict:
-    """
-    Function to read the variables.json file specified by the user
-    :param variables_path: A Path object of the user-specified file path
-    :returns:
-            - dict - A valid dictionary consisting of the causal tests
-    """
-    if not variables_path.exists() or variables_path.is_dir():
-        print(f"JSON file not found at the specified location: {variables_path}")
-
-        raise FileNotFoundError
-
-    with variables_path.open("r") as file:
-        inputs = json.load(file)
-
-    return inputs
-
-
-def validate_variables(data_dict: dict) -> tuple:
-    """
-    Function to validate the variables defined in the causal tests
-    :param data_dict: A dictionary consisting of the pre-defined variables for the causal tests
-    :returns:
-    - Tuple containing the inputs, outputs and constraints to pass into the modelling scenario
-    """
-    if data_dict["variables"]:
-        variables = data_dict["variables"]
-
-        inputs = [
-            Input(variable["name"], eval(variable["datatype"]))
-            for variable in variables
-            if variable["typestring"] == "Input"
-        ]
-
-        outputs = [
-            Output(variable["name"], eval(variable["datatype"]))
-            for variable in variables
-            if variable["typestring"] == "Output"
-        ]
-
-        constraints = set()
-
-    else:
-        raise ValidationError("Cannot find the variables defined by the causal tests.")
-
-    return inputs, outputs, constraints
-
-
-def main():
-    """
-    Main entrypoint of the script:
-    """
-    args = get_args()
-
-    try:
-        # Step 0: Read in the runtime dataset(s)
-
-        data_frame = pd.concat([pd.read_csv(d) for d in args.data_path])
-
-        # Step 1: Read in the JSON input/output variables and parse io arguments
-
-        variables_dict = read_variables(args.variables_path)
-
-        inputs, outputs, constraints = validate_variables(variables_dict)
-
-        # Step 2: Set up the modeling scenario and estimator
-
-        modelling_scenario = Scenario(variables=inputs + outputs, constraints=constraints)
-
-        modelling_scenario.setup_treatment_variables()
-
-        estimators = {
-            "LinearRegressionEstimator": LinearRegressionEstimator,
-            "LogisticRegressionEstimator": LogisticRegressionEstimator,
-        }
-
-        # Step 3: Define the expected variables
-
-        expected_outcome_effects = {
-            "Positive": Positive(),
-            "Negative": Negative(),
-            "NoEffect": NoEffect(),
-            "SomeEffect": SomeEffect(),
-        }
-
-        # Step 4: Call the JSONUtility class to perform the causal tests
-
-        json_utility = JsonUtility(args.output_path, output_overwrite=True)
-
-        # Step 5: Set the path to the data.csv, dag.dot and causal_tests.json file
-        json_utility.set_paths(args.tests_path, args.dag_path, args.data_path)
-
-        # Step 6: Sets up all the necessary parts of the json_class needed to execute tests
-        json_utility.setup(scenario=modelling_scenario, data=data_frame, ignore_cycles=args.ignore_cycles)
-
-        # Step 7: Run the causal tests
-        test_outcomes = json_utility.run_json_tests(
-            effects=expected_outcome_effects, mutates={}, estimators=estimators, f_flag=args.f
-        )
-
-        # Step 8: Update, print and save the final outputs
-
-        for test in test_outcomes:
-            test.pop("estimator")
-
-            test["result"] = test["result"].to_dict(json=True)
-
-            test["result"].pop("treatment_value")
-
-            test["result"].pop("control_value")
-
-        with open(args.output_path, "w", encoding="utf-8") as f:
-            print(json.dumps(test_outcomes, indent=2), file=f)
-
-        print(json.dumps(test_outcomes, indent=2))
-
-    except ValidationError as ve:
-        print(f"Cannot validate the specified input configurations: {ve}")
-
-    else:
-        print(f"Execution successful. " f"Output file saved at {Path(args.output_path).parent.resolve()}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/dafni/model_definition.yaml b/dafni/model_definition.yaml
index 901e2570..a9e2e2ce 100644
--- a/dafni/model_definition.yaml
+++ b/dafni/model_definition.yaml
@@ -1,5 +1,5 @@
 # Model definition file to run the causal testing framework on DAFNI
-# https://docs.secure.dafni.rl.ac.uk/docs/how-to/how-to-write-a-model-definition-file
+# https://docs.secure.dafni.rl.ac.uk/docs/How%20to/how-to-write-a-model-definition-file
 
 kind: M
 api_version: v1beta3
@@ -20,6 +20,9 @@ metadata:
   contact_point_email: farhad.allian@sheffield.ac.uk
   source_code: https://github.com/CITCOM-project/CausalTestingFramework
   licence: https://github.com/CITCOM-project/CausalTestingFramework?tab=MIT-1-ov-file#readme
+  rights: If you use our model, please use the following instructions to cite our work - The paper citation should be the Causal Testing Framework paper, and the software citation should contain the specific Figshare DOI of the version used in your work.
+  project_name: CITCOM
+  funding: The Causal Testing Framework is supported by the UK's Engineering and Physical Sciences Research Council (EPSRC), with the project name CITCOM - "Causal Inference for Testing of Computational Models" under the grant EP/T030526/1.
 
 
 spec:
@@ -49,17 +52,9 @@ spec:
         path: inputs/
         required: true
 
-      - name: Variables
-        description: >
-          A .JSON file containing the input variables to be used
-        default:
-          - 02e755c8-952b-461a-a914-4f4ffbe2edf1
-        path: inputs/
-        required: true
-
   outputs:
-      datasets:
-        - name: causal_test_results.json
-          type: json
-          description: >
-            A JSON file containing the output causal test results.
\ No newline at end of file
+    datasets:
+      - name: causal_test_results.json
+        type: json
+        description: >
+          A JSON file containing the output causal test results.
\ No newline at end of file