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A Causal AI Suite for Decision-Making
Emre Kiciman · Eleanor Dillon · Darren Edge · Adam Foster · Joel Jennings · Chao Ma · Robert Ness · Nick Pawlowski · Amit Sharma · Cheng Zhang
Event URL: https://openreview.net/forum?id=-gVJ1_lD1RH »

Critical data science and decision-making questions across a wide variety of domains are fundamentally causal questions. The causal AI research area is still early in its development, however, and as with any technology area, will require many more advances and iterative practical deployments to reach its full impact. 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.

Author Information

Emre Kiciman (Microsoft Research)
Eleanor Dillon (Microsoft)
Darren Edge (Microsoft Research)
Adam Foster (Microsoft)
Joel Jennings (Microsoft Research)
Chao Ma (University of Cambridge)
Robert Ness (Microsoft Research)

I work on algorithms for machine learning, with emphasis on causal inference, probabilistic modeling, and sequential decision-making algorithms. I currently work on probabilistic programming approaches to natural language processing.

Nick Pawlowski (Microsoft Research)
Amit Sharma (Microsoft Research)
Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

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