This package is for creating Structural Causal Models (SCMs) in Pyro and evaluating various conditions with those models.

Install directly from GitHub with:

$ pip install git+https://github.com/bel2scm/bel2scm.git

Install in development mode with:

$ git clone https://github.com/bel2scm/bel2scm
$ cd bel2scm
$ pip install --editable .

Where --editable symlinks the git repository into your python's site-packages/ directory so you don't have to reinstall on changes.

• All python code goes in src/bel2scm/
• All Jupyter notebooks go in notebooks/. Code in Jupyter notebooks should be refactored into the bel2scm package such that most of the code in the notebook just imports code, does I/O, then makes visualizations.

The causal model (example) can be created from a list of BEL statements strings ( causal_graph.str_graph; http://biological-expression-language.github.io), a PyBEL graph ( causal_graph.bel_graph; https://pypi.org/project/pybel/), or a json file created by exporting a causal graph from Causal Fusion (causal_graph.cf_graph; https://causalfusion.net/login). Each causal model consists of nodes connected by directed edges. Each node then has parameters defining the distribution of that node's variables conditioned on the values of the parent nodes. These parameters are learned from data -- each graph can learn these parameters either using Maximum Likelihood Estimation (for point estimates) or Stochastic Variational Inference (for Bayesian estimates). Currently, Bernoulli, Normal, Lognormal, Exponential, and Gamma output distributions are supported; the choice of distribution is either specified during the initialization or defaults to a hard-coded mapping from BEL object types to distributions.

Once the training process is complete, the causal model can be queried in several different fashions. The basic query is to sample all of the nodes of the model and return a dictionary of node names and samples (example.model_sample). Using built-in Pyro functionality, the model can then leverage this to calculate conditioned samples ( example.model_cond_sample), interventional samples using the do-calculus (example.model_do_sample, example.model_do_cond_sample), and counterfactual samples (example.model_counterfact).

The package includes a method to calculate the Conditional Mutual Information of a target node with respect to a test node of interest (example.cond_mut_info). This calculation relies only on the input data, not the model itself. However, the SCM also has a built-in method to perform the G-test on a variable of interest (example.g_test) to determine if the SCM sufficiently captures the distribution represented by the provided data. Note that performing both of these calculations requires binning the data to produce discrete distributions.

With the various methods for sampling conditional, interventional, and counterfactual distributions from the model, the SCM can estimate the Controlled Direct Effect (example.cd_effect), the Natural Direct Effect (example.nd_effect), and the Natural Indirect Effect (example.ni_effect). Finally, the SCM can write itself to a json file that can then be imported directly to Causal Fusion (example.write_to_cf).

After cloning the repository and installing tox with pip install tox, the unit tests in the Tests/ folder can be run reproducibly with:

$ tox -e finish

Additionally, these tests are automatically re-run with each commit in a GitHub action.

After installing the package in development mode and installing tox with pip install tox, the commands for making a new release are contained within the finish environment in tox.ini. Run the following from the shell:

$ tox -e finish

This script does the following:

1. Uses BumpVersion to switch the version number in the setup.cfg and src/bel2scm/version.py to not have the -dev suffix
2. Packages the code in both a tar archive and a wheel
3. Uploads to PyPI using twine. Be sure to have a .pypirc file configured to avoid the need for manual input at this step
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion minor after.