Carnegie Mellon University

Causality workbench: Isabelle Guyon

Abstract: Challenges have recently proved a great stimulus for research in machine learning, pattern recognition, and robotics. Attracting hundreds of participants and the attention of a broad audience of specialists as well as sometimes the general public, these events have been important in several respects: (1) pushing the state-of-the art, (2) identifying techniques which really work, (3) attracting new researchers, (4) raising the standards of research, (5) giving the opportunity to non-established researchers to make themselves rapidly known.

Since 2007, we have been organizing challenges in causal discovery. To that end, we have set up a dedicated challenge platform, the Causality Workbench. Via a web portal (http: //, the Causality Workbench provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. Our effort has been gaining momentum with the organization of three challenges, attracting in total several hundreds of participants. Our challenge websites, which remain open for post-challenge submissions, are constantly in use by students and have been used in practical work in our own classes and those of other professors throughout the world. We take great care of giving to the participants opportunities publish in reputable conferences proceedings or journals like JMLR. We think of challenges as a means of carrying out research in causal discovery by focusing the mental energy of brilliant researchers around the world. But, what makes a good challenge that provides conclusive results having an important impact? This presentation will review the main findings of our past challenges and look upon them with a critical eye to identify strength and weaknesses and new directions.