************************************************************** ** Call for Papers **
** The 2020 NeurIPS Workshop on ** Causal Discovery and Causality-Inspired Machine Learning ** December 11 or December 12, 2020 (TBD) ** ** Held in conjunction with NeurIPS'20 ** ************************************************************** 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 leverages ideas from causality to improve generalization, robustness, interpretability, and sample efficiency and is attracting more and more interests. 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. *** Topics of Interest* There are two tracks of submissions: paper track and dataset track. For the *paper track*, we invite submissions on all topics of causal discovery and causality-inspired ML, including but not limited to: · Causal discovery in complex environments, e.g., in the presence of distribution shifts, latent confounders, selection bias, cycles, measurement error, small samples, or missing data · Efficient causal discovery in large-scale datasets · Real-world applications of causal discovery, e.g. in neuroscience, finance, climate, and biology · Assessment of causal discovery methods and benchmark datasets · Causal effect estimation with graphical models or from the potential-outcome perspective · Causal perspectives on and solutions to transfer learning, life-long learning, active learning, reinforcement learning, disentanglement, representation learning, developing safe AI, etc. For the *dataset track*, we invite submissions of datasets from various fields, e.g., neuroscience, biology, finance, and climate, that are appropriate for evaluating the performance of causal discovery methods. All accepted papers and data sets will be available on the workshop website. At the end of your paper submission, please indicate whether you would like an extended version of the submission to be considered for publication in a journal special issue. *** Important Dates* · Submission deadline: October 10, 2020 · Notification: October 23, 2020 · Camera-ready and slides: October 30, 2020 · Video submission: November 14, 2020 · Workshop: December 11 or December 12th, 2020 *** Workshop Organizers* Biwei Huang, Carnegie Mellon University Sara Magliacane, IBM Research Kun Zhang, Carnegie Mellon University Danielle Belgrave, Microsoft Research Elias Bareinboim, Columbia University Daniel Malinsky, Columbia University Thomas Richardson, University of Washington Christopher Meek, Microsoft Research Peter Spirtes, Carnegie Mellon University Bernhard Schölkopf, Max-Planck Institute *** Further Information* Please visit workshop website: https://www.cmu.edu/dietrich/causality/neurips20ws/
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