fix: updated docs

Author: f-allian

README.md

@@ -76,7 +76,7 @@ git checkout tags/<tag> -b <branch>
 pip install . # For core API only
 pip install -e . # For editable install, useful for development work
 ```
-For more information on how to use the Causal Testing Framework, please refer to our [documentation](https://causal-testing-framework.readthedocs.io/en/latest/?badge=latest).
+For more information on how to use the Causal Testing Framework, please refer to our [documentation](https://causal-testing-framework.readthedocs.io/en/latest/?badge=latest). If you have any questions, you can also reach us by [email](citcom-group@sheffield.ac.uk).
 
 >[!NOTE]
 >We recommend you use a 64-bit OS (standard in most modern machines) as we have had reports of the installation crashing on legacy 32-bit Debian systems.
@@ -89,12 +89,12 @@ For more information on how to use the Causal Testing Framework, please refer to
 
 2. If you do not already have causal test cases, you can convert your causal DAG to causal tests by running the following command.
 ```
-python -m causal_testing generate --dag_path $PATH_TO_DAG --output_path $PATH_TO_TESTS
+python -m causal_testing generate --dag-path $PATH_TO_DAG --output $PATH_TO_TESTS
 ```
 
 3. You can now execute your tests by running the following command.
 ```
-python -m causal_testing test --dag_path $PATH_TO_DAG --data_paths $PATH_TO_DATA --test_config $PATH_TO_TESTS --output $OUTPUT
+python -m causal_testing test --dag-path $PATH_TO_DAG --data-paths $PATH_TO_DATA --test-config $PATH_TO_TESTS --output $OUTPUT
 ```
 The results will be saved for inspection in a JSON file located at `$OUTPUT`.
 In the future, we hope to add a visualisation tool to assist with this.

docs/source/index.rst

@@ -19,6 +19,8 @@ the inputs and outputs of the system under test, supported by mathematical found
 enable causal inference. Each causal test case targets the causal effect of a specific intervention on the system under test--that is,
 a deliberate modification to the input configuration expected to produce a corresponding change in one or more outputs.
 
+If you have any questions about our framework, you can also reach us by [email](citcom-group@sheffield.ac.uk).
+
 .. toctree::
    :hidden:
    :caption: Home
@@ -72,8 +74,9 @@ a deliberate modification to the input configuration expected to produce a corre
 
    CITCoM Homepage <https://sites.google.com/sheffield.ac.uk/citcom/home>
    Paper <https://dl.acm.org/doi/10.1145/3607184>
-   Figshare <https://orda.shef.ac.uk/articles/software/CITCOM_Software_Release/24427516>
    PyPI <https://pypi.org/project/causal-testing-framework/>
+   Conda-forge <https://anaconda.org/channels/conda-forge/packages/causal-testing-framework/overview>
+   Figshare <https://orda.shef.ac.uk/articles/software/CITCOM_Software_Release/24427516>
    DeepWiki <https://deepwiki.com/CITCOM-project/CausalTestingFramework>
 
 .. toctree::

docs/source/installation.rst

@@ -93,6 +93,55 @@ Next Steps
 
 * Check out the :doc:`tutorials` to learn how to use the framework.
 * Read about :doc:`modules/causal_specification` to understand causal specifications and :doc:`modules/causal_testing` for the end-to-end causal testing process.
-* Try the command-line interface for quick and simple testing::
+* Run the command for guidance on how to generate your causal tests directly from your input DAG::
 
-    python -m causal_testing test --help
+    python -m causal_testing generate --help
+
+* and the command on guidance on how to execute your causal tests::
+
+    python -m causal_testing test --help
+
+
+Using the CTF on DAFNI
+======================
+
+The Causal Testing Framework is also available to run on `DAFNI <https://www.dafni.ac.uk/>`_, allowing you to generate causal tests and evaluate causal effects from your input data and DAGs without installing the framework locally. This lets you integrate CTF into workflows with other models or datasets easily.
+
+Data tab
+--------
+
+- Upload the required input files as a dataset. Typically, this includes:
+
+  - ``dag.dot`` – the directed acyclic graph defining causal relationships between variables.
+  - ``runtime_data.csv`` – the CSV file containing runtime input data.
+  - ``causal_tests.json`` – optional; if provided, the framework will run tests directly. Otherwise, tests will be generated automatically.
+
+  **Note:** Input files must remain in the ``data/inputs`` structure; this is required by the workflow.
+
+Workflow tab
+-------------
+
+- Select the CTF workflow.
+- In the Parameter sets section, click **Create**.
+- In the page that opens:
+
+  - Select the model in the workflow (typically ``causal-testing-framework``).
+  - Complete the sections at the bottom:
+
+    - **Parameters:** Set or confirm environment variables from the ``.env`` file (e.g., ``EXECUTION_MODE``, ``CAUSAL_TESTS``, ``CAUSAL_TEST_RESULTS``). These control whether tests are generated or executed, the filenames, estimator, effect type, and other runtime options.
+    - **Datasets:** Click the icon and select the dataset containing your input files (``dag.dot``, ``runtime_data.csv``, ``causal_tests.json``). All input files will be placed in the required ``data/inputs`` directory when running the workflow.
+
+- Unselect the model if needed, click **Continue**, and complete any required metadata such as the name of the parameter set.
+
+Execute the workflow
+----------------------
+
+- Click **Execute workflow with parameter set**.
+- If successful, the workflow will either generate ``causal_tests.json`` (if not provided) or run the tests and create ``causal_test_results.json`` in ``data/outputs``.
+- After completion, you can view the results in the **Data tab** as a new output dataset.
+
+Customisation and chaining
+--------------------------
+
+- You can create additional workflows to customise input parameters, filenames, or estimators.
+- Multiple CTF workflows can also be chained to run sequential analyses or to integrate with other models and datasets, combining results for more complex causal testing scenarios.