A Causal AI Suite for Decision-Making

Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

Keywords: causal tools, library, discovery, identification, inference, refutation

TL;DR: We present a suite of causal tools and libraries that provides core causal AI functionality to practitioners and creates a platform for research advances to be rapidly deployed.

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.