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:
5
6
7
8
Coding:
9
10
Information:
11
12
13
Last but not least, we encourage developers to submit your own issues to challenge us!
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:
5
6
7
8
Coding:
9
10
Information:
11
12
13
Last but not least, we encourage developers to submit your own issues to challenge us!