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Workshop: Causal Machine Learning for Real-World Impact

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


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.

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