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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 11 06:50 AM -- 04:50 PM (PST) @ None
Event URL: https://www.cmu.edu/dietrich/causality/neurips20ws/ »

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.

Fri 6:50 a.m. - 7:00 a.m.
Opening Remarks (Opening remarks)
Fri 8:10 a.m. - 8:40 a.m.
Coffee break (Break)
Fri 9:30 a.m. - 10:00 a.m.
Coffee break (Break)
Fri 10:30 a.m. - 11:30 a.m.
Poster Session 1 (poster session)
Fri 11:30 a.m. - 12:00 p.m.
Coffee Break (Break)
Fri 1:00 p.m. - 1:30 p.m.
Coffee Break (Break)
Fri 2:10 p.m. - 2:40 p.m.
Coffee Break (Break)
Fri 3:40 p.m. - 4:40 p.m.
Poster Session 2 (poster session)
Fri 4:40 p.m. - 4:50 p.m.
Closing Remarks (closing remarks)
Keynotes: Clark Glymour (Invited talk)
Clark Glymour
Keynotes: James Robins (Invited talk)
james m robins
Keynotes: Caroline Uhler (Invited talk)
Caroline Uhler
Keynotes: Dominik Janzing (Invited talk)
Dominik Janzing
Keynotes: Shohei Shimizu (Invited talk)
Shohei Shimizu
Keynotes: Karthika Mohan (Invited talk)
Karthika Mohan
Keynotes: Aapo Hyvärinen (Invited talk)
Aapo Hyvarinen
Oral: Debarun Bhattacharjya (Oral talk)
Debarun Bhattacharjya
Oral: Ignavier Ng (Oral talk)
Ignavier Ng
Oral: Tineke Blom (Oral talk)
Tineke Blom
Oral: Panayiotis Benos (Oral talk)
Takis Benos

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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