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 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.

Invited Speakers

Organizing Committee

Call for Submissions

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
  • Causal effect identification and estimation
  • Real-world applications of causal discovery, e.g. in neuroscience, finance, climate, and biology
  • Assessment of causal discovery methods and benchmark datasets
  • Causal perspectives on the problem of generalizability, transportability, transfer learning, and life-long learning
  • Causally-enriched reinforcement learning and active learning
  • Disentanglement, representation learning, and developing safe AI from a causal perspective

Submitted papers should follow the requirements for NeurIPS 2020 submissions. The length of submissions is flexible, but is limited to eight content pages, including all figures and tables; additional pages containing only references are allowed. Please format your submission using the NeurIPS 2020 LaTeX style file. If needed, authors may additionally submit a supplementary material. According to the workshop guidance provided by the conference, “work that is presented at the main NeurIPS conference should not appear in a workshop, including as part of an invited talk.”

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. Submissions should include (1) the collected dataset (the file or a link is required) and (2) a description of the dataset in PDF format (with NeurIPS 2020 LaTeX style file), limited to four pages. The description should include the “ground truth” causal structure from domain knowledge or experiments and the testing results of at least one causal discovery method.

All accepted papers and datasets 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. According to the feedback from authors, we will further decide whether to publish selected papers in proceedings or a journal special issue.

Key Dates

  • Submission: October 14, 2020
  • Notification: October 30, 2020
  • Camera-ready and slides: November 14, 2020
  • Video submission: November 14, 2020
  • Workshop: December 11 or December 12th, 2020

Submission Website

Papers and datasets can be submitted through CMT: https://cmt3.research.microsoft.com/CDML2020/.

Double-blind Review

Authors must not include any identifying information of the authors (names, affiliations, etc.) or links and self-references that may reveal the authors' identities. The organizers aim to provide feedback from three reviewers per submission, which will assess the submission based on relevance, novelty, and potential for impact. Reviewers are asked to assess the submission (Reject/Borderline/Accept) as well as provide written feedback. There will be no additional rebuttal period.

Schedule

The workshop will consist of four main parts: pre-recorded invited talks with a live discussion, pre-recorded contributed presentations with a live discussion, a virtual poster session with spotlight talks, and, to conclude, a panel discussion. We will have seven invited talks, covering recent developments in causal discovery and inference, the connection between causal modeling and machine learning, and applications of causal analysis. Importantly, we will make all videos available on our website one week before the workshop, so attendees in all time zones can watch them beforehand and submit questions. To transfer the social and forum-like aspects of in-person workshops to our virtual workshop, during the workshop day we will have a dedicated Slack channel for introductions and virtual coffee breaks in which we will try to foster social interaction and discussion on Slack and Zoom.

We will select four papers among all accepted ones for 10-minute contributed talks. Each paper to be presented in the poster session will have a 2-minute spotlight introduction, followed by a poster exhibition during lunch break and after closing remarks. Finally, in the panel discussion, we will ask our invited speakers to discuss questions from the audience that are collected beforehand.

The detailed schedule will come soon.

How to Participate

Coming soon…

Program Committee

Area Chairs: AmirEmad Ghassami, Antti Hyttinen, Biwei Huang, Christopher Meek, Daniel Malinsky, Dominik Janzing, Frederick Eberhardt, Ilya Shpitser, Mingming Gong, Peter Spirtes, Ruichu Cai, Sanghack Lee, Sara Magliacane, Shohei Shimizu, Tom Claassen, Yangbo He

Program Committee Members: AmirEmad Ghassami, Amir-Hossein Karimi, Biwei Huang, Bryan Andrews, Daniel Malinsky, Debarun Bhattacharjya, Eli Sherman, Erich Kummerfeld, Feng Xie, Ignavier Ng, Jaron Lee, Joseph Ramsey, Julius Kügelgen, Junzhe Zhang, Karthikeyan Shanmugam, Kenney Ng, Kun Kuang, Lin Liu, Mingming Gong, Murat Kocaoglu, Petar Stojanov, Ran Wang, Razieh Nabi, Rohit Bhattacharya

Sponsors