This repository contains the source code of Causal Decision Making, an online tutorial with the goal of providing practitioners a systematic review, map, and handbook for finding the appropriate solutions for their problems. The tutorial surveys and discusses classic methods and recent advanced in the the off-policy learning literature, and complements the CausalDM Python package where open-source implementations are provided under a unified API.
We welcome and appreciate the community's contributions and suggestions. There are several ways to contribute to CasualDM:
- Adding a Jupyter Notebook that describes newly developed or classical methods that are not yet included, related to either CSL, CEL, or CPL.
- Adding Python codes for CDM learners to extend the CausalDM API;
- Improving or revising documentation.
To add or update a Jupyter Notebook, submit a pull request. If you are unfamiliar with request pulling or have any questions before contributing, you can begin by creating an issue on Github and attaching the notebook you want to upload, as well as 2-3 sentences describing which step the method is related to and which diagram it is used for. To contribute via a request pull, you must first fork the repository and create a new branch to make changes. If you would like to contribute to the CausalDM code package, please contact the main authors for more information.
- Describe the main ideas, advantages, and appropriate use cases
- Introduce the key formulae (not be heavy in notation) and algorithms (pseudo code)
- (Optional) Demo Code to illustrate how to use the package, if you've already added the correspondng code to the CausalDM pythong package
See 0_Learner Template.ipynb in the main folder for a template. Once you've finished preparing the Jupyter Notebook, you need to choose which chapter/folder to add it to by determining which of the three steps and six paradigms the method belongs to and providing this information when requesting a pull. Once the pull request is received, the main authors will take charge of adding it to the online book's table of contents and publishing it online.
Thanks goes to these wonderful people (emoji key):
 Lin Ge 💻 📖 |
 RunzheStat 💻 📖 |
 Hengrui Cai 💻 📖 |
 YangXU63 💻 📖 |
 Rui Song 📖 |
 Shadow 📖 |
 Xiaodong WANG 🐛 |