Getting Started

Installation

We currently support Python versions 3.11, 3.12, 3.13, and 3.14.
The Causal Testing Framework can be installed through conda-forge (recommended), the Python Package Index (PyPI), or directly from source (recommended for contributors).

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.

Method 1: Installing via conda-forge (Recommended)

We recommend using conda or mamba for installation, as they provide better dependency management and environment isolation, particularly for scientific computing workflows.

First, create a new conda environment with a supported Python version, e.g:

conda create -n causal-testing-env python=3.13
conda activate causal-testing-env

Note

If you have Miniforge installed, you can replace conda with mamba in any of the commands below for faster package resolution.

Add the conda-forge channel:

conda config --add channels conda-forge
conda config --set channel_priority strict

Install causal-testing-framework:

conda install causal-testing-framework

Method 2: Installing via pip

If you prefer using pip or need the development packages, you can install from PyPI.

To install the Causal Testing Framework using pip for the latest stable version:

pip install causal-testing-framework

This provides all core functionality needed to perform causal testing, including support for causal DAGs, various estimation methods, and test case generation.

If you also want to install the framework with (optional) development packages/tools:

pip install causal-testing-framework[dev]

Method 3: Installing via Source (For Developers/Contributors)

If you’re planning to contribute to the project or need an editable installation for development, you can install directly from source:

git clone https://github.com/CITCOM-project/CausalTestingFramework
cd CausalTestingFramework

then, to install a specific release:

git fetch --all --tags --prune
git checkout tags/<tag> -b <branch>
pip install . # For core API only
pip install -e . # For editable install, useful for development work

e.g. version 1.0.0:

git fetch --all --tags --prune
git checkout tags/1.0.0 -b version
pip install .

or to install the latest development version:

pip install .

To also install developer tools:

pip install -e .[dev]

Verifying Your Installation

After installation, verify that the framework is installed correctly in your environment:

python -c "import causal_testing; print(causal_testing.__version__)"

Next Steps

Check out the Tutorials to learn how to use the framework.
Read about Causal Specification to understand causal specifications and Causal Testing for the end-to-end causal testing process.
Run the command for guidance on how to generate your causal tests directly from your input DAG:
```
causal-testing generate --help
```
and the command on guidance on how to execute your causal tests:
```
causal-testing test --help
```

Using the CTF on DAFNI

The Causal Testing Framework is also available to run on DAFNI, 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.