Ananke: A module for causal inference

How to install

Requires Python 3.7 or higher. Run the following command:

pip3 install ananke-causal

Check out the gitlab!

Documentation

Contents:

• Quickstart
  • Creating a Causal Graph
  • Identification of the Causal Effect
  • Estimation of the Causal Effect
• Causal Inference with Graphical Models
• Semiparametric Inference For Causal Effects
  • Estimation via Adjustment
  • Estimation beyond Adjustment
  • Useful Graphical Criteria
  • Accessing the Functional Form of the Influence Function
• Identification With Surrogates
  • GID
  • AID
• Linear Structural Equation Models
  • Arid Graphs
  • Fitting and Causal Effect Estimation
  • Model Selection

Citation

If you enjoyed this package, we would appreciate the following citations:

[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

Ananke Graphs

ananke.graphs package

Ananke Identification

ananke.identification package

Ananke Estimation

ananke.estimation package

Ananke Models

ananke.models package

Indices and Tables

• Index
• Module Index
• Search Page