cause2e 0.1.1

A package for end to end causal analysis

The cause2e package provides tools for performing an end-to-end causal analysis of your data. If you have data and domain knowledge about the data generating process, it allows you to:

• Learn a graphical causal model of the data generating process.
• Identify a statistical estimand for the causal effect that one variable has on another variable.
• Estimate the effect with various statistical techniques.
• Check the robustness of your results with respect to changes in the causal model.

The main contribution of cause2e is the integration of two established causal packages that have currently been separated and cumbersome to combine:

• Causal discovery methods from the py-causal package [1], which is a Python wrapper around parts of the Java TETRAD software. It provides many algorithms for learning the causal graph from data and domain knowledge.

• Causal reasoning methods from the DoWhy package [2], which is the current standard for the steps of a causal analysis starting from a known causal graph and data:

  • Algebraically identifying a statistical estimand for a causal effect from the causal graph via do-calculus.
  • Using statistical estimators to actually estimate the causal effect.
  • Performing robustness tests to check how sensitive the estimate is to model misspecification and other errors.

cause2e provides an easy to use API for performing an end-to-end causal analysis without having to worry about fitting together different libraries and data structures for causal discovery and causal reasoning:

• The StructureLearner class for causal discovery can

  • read and preprocess data
  • accept domain knowledge in a simple data format
  • learn the causal graph using py-causal algorithms
  • manually postprocess the resulting graph in case you want to add, delete or reverse some edges
  • check if the graph is acyclic and respects the domain knowledge
  • save the graph to various file formats

• The Estimator class for causal reasoning can

  • read data and imitate the preprocessing steps applied by the StructureLearner
  • load the causal graph that was saved by the StructureLearner
  • perform the above mentioned causal reasoning steps suggested by the DoWhy package

Additonally, cause2e offers helper classes for handling all paths to your data and output, representing domain knowledge and generating synthetic data for benchmarking.

Documentation:

For a detailed documentation of the package, please refer to https://cause2e.readthedocs.io. The documentation has been generated from Python docstrings via Sphinx.

Outlook:

We are planning to integrate the causal discovery toolbox [3] as a second collection of causal discovery algorithms. In the spirit of end-to-end causal analysis, it would also be desirable to include causal representation learning before the discovery step (e.g. for image data), or causal reinforcement learning after having distilled a valid causal model that delivers interventional distributions.

Installation:

First, install py-causal by following these instructions: https://github.com/bd2kccd/py-causal

You can then install cause2e from pypi:

pip install cause2e

You can also install it directly from this Github repository:

pip install git+git://github.com/MLResearchAtOSRAM/cause2e

Disclaimer:

cause2e is not meant to replace either py-causal or DoWhy, our goal is to make it easier for researchers to string together causal discovery and causal reasoning with these libraries. If you are only interested in causal discovery, it is preferable to directly use py-causal or the TETRAD GUI. If you are only interested in causal reasoning, it is preferable to directly use DoWhy.

Citation:

If you are using cause2e in your work, please cite:

Daniel Grünbaum (2021). cause2e: A Python package for end-to-end causal analysis. https://github.com/MLResearchAtOSRAM/cause2e

References:

[1] Chirayu (Kong) Wongchokprasitti, Harry Hochheiser, Jeremy Espino, Eamonn Maguire, Bryan Andrews, Michael Davis, & Chris Inskip. (2019, December 26). bd2kccd/py-causal v1.2.1 (Version v1.2.1). Zenodo. http://doi.org/10.5281/zenodo.3592985

[2] Amit Sharma, Emre Kiciman, et al. DoWhy: A Python package for causal inference. 2019. https://github.com/microsoft/dowhy

[3] Kalainathan, D., & Goudet, O. (2019). Causal Discovery Toolbox: Uncover causal relationships in Python. arXiv:1903.02278. https://github.com/FenTechSolutions/CausalDiscoveryToolbox