[CFP] Causal Machine Learning for Real-World Impact @ NeurIPS 2022
Nick Pawlowski
Hi all,
I would like to announce the NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact) and invite submissions of papers.
On behalf of the organisers,
Nick
Website: https://www.cml-4-impact.vanderschaar-lab.com/
Email: cml.for...@gmail.com
Important Dates
Submission Deadline: September 30th, 2022 Anywhere on Earth (AoE)
Acceptance Notifications: October 22nd, 2022
Workshop event: December 2nd, 2022, In-person in New Orleans, LA, USA.
Call for papers:
The 2022 NeurIPS Workshop on Causal Machine Learning for Real-World Impact is calling for contributions combining causality and machine learning, with a view on using these methods to solve real-world problems with practical impact. We invite researchers to submit position papers about real-world causal problems, methodological contributions applying machine learning to solve causal questions, applications of these methods to specific real-world problems, as well as tools to ease the applications by practitioners. Potential interest areas include but are not limited to the following areas:
identifying causal structures in data
estimating causal effects
causal attribution
causal decision making
causal applications in education, healthcare, business decision-making, etc.
tools to ease application of causal machine learning
We invite submissions that either address new problems and provide insights or present progress on established problems. The workshop includes a poster session giving the opportunity to present novel ideas and ongoing projects. Outstanding submissions will be invited for spotlight presentations and qualify for a best paper award. Additionally, all authors are asked to indicate whether they want to be considered for a travel award. The awards are sponsored by Eedi (https://eedi.com/)
Submission Instructions
Submissions should be no more than 6 pages, excluding references and supplementary material. We ask authors to use the supplementary material only for minor details that do not fit in the main paper. We also welcome short papers (~3 pages) that present ongoing work, discuss open problems and challenges, or introduce new tools that ease the use of causal machine learning.
Papers should use this style template and be submitted through OpenReview.
Organizers:
Nick Pawlowski Microsoft Research
Jeroen Berrevoets University of Cambridge
Caroline Uhler MIT
Kun Zhang Carnegie Mellon University
Mihaela van der Schaar University of Cambridge, UCLA, Alan Turing Institute
Cheng Zhang Microsoft Research