Workshop: Causal Discovery and Causality-Inspired Machine Learning

Biwei Huang, Sara Magliacane, Kun Zhang, Danielle Belgrave, Elias Bareinboim, Daniel Malinsky, Thomas Richardson, Christopher Meek, Peter Spirtes, Bernhard Schölkopf

Fri, Dec 11th @ 14:50 GMT – Sat, Dec 12th @ 00:50 GMT
Abstract: Causality is a fundamental notion in science and engineering, and one of the fundamental problems in the field is how to find the causal structure or the underlying causal model. For instance, one focus of this workshop is on *causal discovery*, i.e., how can we discover causal structure over a set of variables from observational data with automated procedures? Another area of interest is *how a causal perspective may help understand and solve advanced machine learning problems*.

Recent years have seen impressive progress in theoretical and algorithmic developments of causal discovery from various types of data (e.g., from i.i.d. data, under distribution shifts or in nonstationary settings, under latent confounding or selection bias, or with missing data), as well as in practical applications (such as in neuroscience, climate, biology, and epidemiology). However, many practical issues, including confounding, the large scale of the data, the presence of measurement error, and complex causal mechanisms, are still to be properly addressed, to achieve reliable causal discovery in practice.

Moreover, causality-inspired machine learning (in the context of transfer learning, reinforcement learning, deep learning, etc.) leverages ideas from causality to improve generalization, robustness, interpretability, and sample efficiency and is attracting more and more interest in Machine Learning (ML) and Artificial Intelligence. Despite the benefit of the causal view in transfer learning and reinforcement learning, some tasks in ML, such as dealing with adversarial attacks and learning disentangled representations, are closely related to the causal view but are currently underexplored, and cross-disciplinary efforts may facilitate the anticipated progress.

This workshop aims to provide a forum for discussion for researchers and practitioners in machine learning, statistics, healthcare, and other disciplines to share their recent research in causal discovery and to explore the possibility of interdisciplinary collaboration. We also particularly encourage real applications, such as in neuroscience, biology, and climate science, of causal discovery methods.

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After each keynote, there will be 5 minutes for a live Q&A. You may post your questions in Rocket.Chat before or during the keynote time. The poster session and the virtual coffee break will be on Gather.Town. There is no Q&A for orals and spotlight talks, but all papers will attend the poster session and you can interact with authors there. More details will come soon.

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Schedule

14:50 – 15:00 GMT
Opening Remarks
15:00 – 15:30 GMT
Keynotes: Aapo Hyvärinen
Aapo Hyvarinen
15:30 – 16:00 GMT
Keynotes: Clark Glymour
Clark Glymour
16:00 – 16:10 GMT
Oral: Panayiotis Benos
Takis Benos
16:10 – 16:40 GMT
Coffee break (social interactions on Gather.Town)
16:40 – 17:10 GMT
Keynotes: James Robins
james m robins
17:10 – 17:20 GMT
Oral: Tineke Blom
Tineke Blom
17:20 – 17:30 GMT
Oral: Debarun Bhattacharjya
Debarun Bhattacharjya
17:30 – 18:00 GMT
Coffee break (social interactions on Gather.Town)
18:00 – 18:30 GMT
Spotlights 1
18:30 – 19:30 GMT
Poster Session 1 (Gather.Town)
19:30 – 20:00 GMT
Coffee break (social interactions on Gather.Town)
20:00 – 20:30 GMT
Keynotes: Dominik Janzing
Dominik Janzing
20:30 – 21:00 GMT
Keynotes: Caroline Uhler
Caroline Uhler
21:00 – 21:30 GMT
Coffee break (social interactions on Gather.Town)
21:30 – 22:00 GMT
Keynotes: Karthika Mohan
Karthika Mohan
22:00 – 22:10 GMT
Oral: Ignavier Ng
Ignavier Ng
22:10 – 22:40 GMT
Coffee break (social interactions on Gather.Town)
22:40 – 23:10 GMT
Keynotes: Shohei Shimizu
Shohei Shimizu
23:10 – 23:40 GMT
Spotlights 2
Fri, Dec 11th @ 23:40 GMT – Sat, Dec 12th @ 00:40 GMT
Poster Session 2 (Gather.Town)
00:40 – 00:50 GMT
Closing Remarks