sempler 0.2.14

Sample from general structural causal models (SCMs).

Sempler: generate synthetic and realistic semi-synthetic data with known ground truth for causal discovery

[Documentation at https://sempler.readthedocs.io/en/latest/]

Sempler allows you to generate synthetic data from SCMs and semi-synthetic data with known causal ground truth but distributions closely resembling those of a real data set of choice. It is one of the software contributions of the paper "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions" by Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml and Peter Bühlmann. You can find more details in Appendix E of the paper.

If you find this code useful, please consider citing:

@article{gamella2022characterization,
  title={Characterization and greedy learning of Gaussian structural causal models under unknown interventions},
  author={Gamella, Juan L and Taeb, Armeen and Heinze-Deml, Christina and B{\"u}hlmann, Peter},
  journal={arXiv preprint arXiv:2211.14897},
  year={2022}
}

Overview

The semi-synthetic data generation procedure is implemented in the class sempler.DRFNet (see docs). A detailed explanation of the procedure can be found in Appendix E of the paper.

Additionally, you can generate purely synthetic data from general additive-noise models. Two classes are defined for this purpose.

• sempler.ANM is for general (acyclic) additive noise SCMs. Any assignment function is possible, as are the distributions of the noise terms.
• sempler.LGANM is for linear Gaussian SCMs. While this is also possible with sempler.ANM, this class simplifies the interface and offers the additional functionality of sampling "in the population setting", i.e. by returning a symbolic gaussian distribution (see sempler.LGANM.sample and sempler.NormalDistribution).

To allow for random generation of SCMs and interventional distributions, the module sempler.generators contains functions to sample random DAGs and intervention targets.

Installation

You can clone this repo or install using pip. To install sempler in its most basic form, i.e. to generate purely synthetic data with sempler.ANM and sempler.LGANM, simply run

pip install sempler

To install the additional dependencies needed for the semi-synthetic data generation procedure, run

pip install sempler[DRFNet]

which will install sempler with the additional rpy2 dependency. You will also need:

• an R installation; you can find an installation guide here
• the R package drf, which you can install by typing install.packages("drf") in an R terminal

Versioning

Sempler is still at its infancy and its API is subject to change. Non backward-compatible changes to the API are reflected by a change to the minor or major version number,

e.g. code written using sempler==0.1.2 will run with sempler==0.1.3, but may not run with sempler==0.2.0.

Documentation

You can find the full documentation at https://sempler.readthedocs.io/en/latest/.

Feedback

Feedback is most welcome! You can add an issue or send an email.