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.
Ananke is licensed under Apache 2.0 and source code is available at gitlab.
Citation
If you enjoyed this package, we would appreciate the following citation:
- LBNS23
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:
- BNS20
Rohit Bhattacharya, Razieh Nabi, and Ilya Shpitser. Semiparametric inference for causal effects in graphical models with hidden variables. arXiv preprint arXiv:2003.12659, 2020.
- LS20
Jaron J. R. Lee and Ilya Shpitser. Identification Methods With Arbitrary Interventional Distributions as Inputs. arXiv preprint arXiv:2004.01157 [cs, stat], 2020.
- NBS20
Razieh Nabi, Rohit Bhattacharya, and Ilya Shpitser. Full law identification in graphical models of missing data: completeness results. arXiv preprint arXiv:2004.04872, 2020.
Contributors
Rohit Bhattacharya
Jaron Lee
Razieh Nabi
Preethi Prakash
Ranjani Srinivasan
Documentation
Getting Started