This module implements a set of functions to perform MAXENT from a causal perspective.
The code here can be used to reproduce the results in the publication Obtaining Causal Information by Merging Datasets with MAXENT. The parts of the plots using KCI are, unfortunately, not available. To reproduce the results in the article create a python 3.6+ environment, pip install all the requirements.txt file and export the cmaxent folder to your python path (when the environment is activated) in your command line, like this:
export PYTHONPATH="/directions/to/the/folder/cmaxent"
Finally, you should make sure that there is a results folder inside the experiments folder. Then you can reproduce all the results as easy as running the individual experiment files. For example, for the ROC curves, you can run in your command line:
python3 ./experiments/polynomial_experiment.py
If you use this code in your own research, please cite our paper
Garrido Mejia, S.H., Kirschbaum, E. and Janzing, D., 2022, May. Obtaining causal information by merging datasets with maxent. In International Conference on Artificial Intelligence and Statistics (pp. 581-603). PMLR.
If you are interested in the origins of our work, we recommend the following readings, in addition to our AISTATS article:
From a non-causal perspective:
- E.T. Jaynes (1957) Information theory and statistical mechanics: https://bayes.wustl.edu/etj/articles/theory.1.pdf
- Wainwright and Jordan (2008) Graphical Models, Exponential Families, and Variational Inference: https://people.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf
From a causal perspective:
- Sun et.al. (2005) Causal inference by choosing graphs with most plausible Markov kernels: http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/AIMath2006-Sun_[0].pdf
- Janzing et.al. (2009) Distinguishing Cause and Effect via Second Order Exponential Models: https://arxiv.org/pdf/0910.5561.pdf
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.