Ananke: A module for causal inference
Ananke, named for the Greek primordial goddess of necessity and causality, is a Python package for causal inference using the language of graphical models.
Ananke provides a Python implementation of causal graphical models with and without unmeasured confounding, with a particular focus on causal identification, semiparametric estimation, and parametric likelihood methods.
If you enjoyed this package, we would appreciate the following citation:
Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi, and Ilya Shpitser. Ananke: a python package for causal inference using graphical models. 2023. arXiv:2301.11477.
Additional relevant citations also include:
Rohit Bhattacharya, Razieh Nabi, and Ilya Shpitser. Semiparametric inference for causal effects in graphical models with hidden variables. arXiv preprint arXiv:2003.12659, 2020.
Jaron J. R. Lee and Ilya Shpitser. Identification Methods With Arbitrary Interventional Distributions as Inputs. arXiv preprint arXiv:2004.01157 [cs, stat], 2020.
Razieh Nabi, Rohit Bhattacharya, and Ilya Shpitser. Full law identification in graphical models of missing data: completeness results. arXiv preprint arXiv:2004.04872, 2020.