Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update README.md - Adding talks and publication info #433

Merged
merged 1 commit into from
Dec 11, 2021
Merged

Update README.md - Adding talks and publication info #433

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -235,6 +235,13 @@ This project is licensed under the Apache 2.0 License - see the [LICENSE](https:
## Documentation
* [Causal ML API documentation](https://causalml.readthedocs.io/en/latest/about.html)

## Conference Talks and Publications by CausalML Team
* (Talk) Introduction to CausalML at [Causal Data Science Meeting 2021](https://www.causalscience.org/meeting/program/day-2/)
* (Talk) Introduction to CausalML at [2021 Conference on Digital Experimentation @ MIT (CODE@MIT)](https://ide.mit.edu/events/2021-conference-on-digital-experimentation-mit-codemit/)
* (Publication) CausalML White Paper [Causalml: Python package for causal machine learning](https://arxiv.org/abs/2002.11631)
* (Publication) [Uplift Modeling for Multiple Treatments with Cost Optimization](https://ieeexplore.ieee.org/document/8964199) at [2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)](http://203.170.84.89/~idawis33/dsaa2019/preliminary-program/)
* (Publication) [Feature Selection Methods for Uplift Modeling](https://arxiv.org/abs/2005.03447)

## Citation
To cite CausalML in publications, you can refer to the following sources:

Expand All @@ -252,7 +259,7 @@ Bibtex:
>}


## Papers
## Literature

1. Chen, Huigang, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).
2. Radcliffe, Nicholas J., and Patrick D. Surry. "Real-world uplift modelling with significance-based uplift trees." White Paper TR-2011-1, Stochastic Solutions (2011): 1-33.
Expand Down