A Causal AI Suite for Decision-Making
Abstract
Critical data science and decision-making questions across a wide variety of domains are fundamentally causal questions. We present a suite of open-source causal tools and libraries that aims to simultaneously provide core causal AI functionality to practitioners and create a platform for research advances to be rapidly deployed. In this paper, we describe our contributions towards such a comprehensive causal AI suite of tools and libraries, its design, and lessons we are learning from its growing adoption. We hope that our work accelerates use-inspired basic research for improvement of causal AI.
Cite
Text
Kiciman et al. "A Causal AI Suite for Decision-Making." NeurIPS 2022 Workshops: CML4Impact, 2022.Markdown
[Kiciman et al. "A Causal AI Suite for Decision-Making." NeurIPS 2022 Workshops: CML4Impact, 2022.](https://mlanthology.org/neuripsw/2022/kiciman2022neuripsw-causal/)BibTeX
@inproceedings{kiciman2022neuripsw-causal,
title = {{A Causal AI Suite for Decision-Making}},
author = {Kiciman, Emre and Dillon, Eleanor Wiske and Edge, Darren and Foster, Adam and Hilmkil, Agrin and Jennings, Joel and Ma, Chao and Ness, Robert and Pawlowski, Nick and Sharma, Amit and Zhang, Cheng},
booktitle = {NeurIPS 2022 Workshops: CML4Impact},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/kiciman2022neuripsw-causal/}
}