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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

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Event URL: https://www.cmu.edu/dietrich/causality/neurips20ws/ »

Causality is a fundamental notion in science and engineering. Much attention has been paid to the estimation of causal effects, where the causal structure is assumed to be given. This raises an important question: how can we find causal structure or causal models? Accordingly, 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 focus is on 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, 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 interests 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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Invited talk_Glymour (Invited talk)
Clark Glymour
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Invited talk_Robins (Invited talk)
james m robins
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Invited talk_Uhler (Invited talk)
Caroline Uhler
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Invited talk_Janzing (Invited talk)
Dominik Janzing
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Invited talk_Shimizu (Invited talk)
Shohei Shimizu
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Invited talk_Mohan (Invited talk)
Karthika Mohan
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Invited talk_Hyvärinen (Invited talk)
Aapo Hyvarinen

Author Information

Biwei Huang (Carnegie Mellon University)
Sara Magliacane (MIT-IBM Watson AI Lab, IBM Research)
Kun Zhang (CMU)
Danielle Belgrave (Microsoft Research)
Elias Bareinboim (Columbia University)
Daniel Malinsky (Johns Hopkins University)
Thomas Richardson (University of Washington)
Christopher Meek (Microsoft Research)
Peter Spirtes (Carnegie Mellon University)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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