Call for Contributors - 2022 July

[LIUZhaohan0]

Welcome to YLearn community!

YLearn is a python package for causal inference which has implemented many recent developed techniques, ranging from causal discovery, causal effect identification and estimation, etc. To keep improving and enhancing the performance, we encourage all interested developers to join and build YLearn.

Below we prepare three types of contribution tasks: documentation, coding, and information. If you are interested and willing to share your ideas, don't hesitate to click the issue and contribute. For our information, please reply to the issue 'I will help this one' to avoid repeat work.

We also provide a detailed instruction in Chinese . For any questions and suggestions, you could also contact us via email: ylearn@zetyun.com

Please find the issues:
Documentation:

• Docs: please translate the ‘prob.rst’ (representation of probability) from English to Chinese #5
• Docs: please translate the ‘meta.rst’ (Meta-Learner) from English to Chinese #6
• Docs: please translate the ‘notears.rst’ (No-Tears) from English to Chinese #7
• Docs: please translate the ‘policy.rst’ (policy) from English to Chinese #8

Coding:

• Code: please build a unit test for function 'graph.py' #9
• Code: please build a unit test for function 'model.py' #10

Information:

• Info: please describe the practical problem you want to solve with YLearn #11
• Info: please provide a causal dataset and describe the background information #12
• Info: please suggest some causally related algorithms and explain why you want to implement them #13

Last but not least, we encourage developers to submit your own issues to challenge us!