Overview
The NeurIPS 2019 workshop on Causal Machine Learning invites researchers to submit their papers. Scheduled to take place in Vancouver on December 13 or 14, this workshop aims to explore the multifaceted aspects of causal inference and its applications. This event is significant for the AI/ML community as it provides a platform for exchanging innovative ideas and methodologies in causal machine learning.
Background & Relevance
Causal machine learning is an emerging field that integrates principles from various disciplines, including statistics, biostatistics, and econometrics. It focuses on understanding causal relationships and making predictions based on counterfactual reasoning. This area is crucial as it enhances the ability to make informed decisions in various domains, including healthcare, social sciences, and economics. The workshop serves as an essential gathering for researchers and practitioners to discuss advancements and challenges in this domain.
Key Details
- Event: NeurIPS 2019 Workshop on Causal Machine Learning
- Dates: December 13 or 14, 2019 (exact date to be confirmed)
- Location: Vancouver
- Submission Deadline: September 9, 2019
- Acceptance Notification: October 1, 2019
- Website: NeurIPS 2019 Causal ML Workshop
Eligibility & Participation
This call for papers is open to researchers from diverse fields who are working on causal inference, counterfactual prediction, and related methodologies. The workshop encourages submissions from various disciplines, highlighting the interdisciplinary nature of causal machine learning.
Submission or Application Guidelines
Interested participants should prepare their manuscripts according to the guidelines provided on the workshop’s website. Submissions should focus on novel research and can include extended abstracts to facilitate the dissemination of work intended for journal publication. Ensure that submissions are made by the deadline of September 9, 2019.
Additional Context / Real-World Relevance
Causal machine learning plays a pivotal role in various applications, from healthcare interventions to policy evaluation. By understanding causal relationships, researchers can develop more effective models and strategies that lead to better outcomes in real-world scenarios. This workshop represents a unique opportunity to engage with leading experts and contribute to the ongoing discourse in this vital area of research.
Conclusion
The NeurIPS 2019 workshop on Causal Machine Learning is an excellent opportunity for researchers to showcase their work and engage with peers in the field. Interested individuals are encouraged to submit their papers and participate in this significant event. Explore the workshop’s website for more details and prepare your submissions ahead of the approaching deadline.
Category: CFP & Deadlines
Tags: causal inference, machine learning, neurips, statistics, biostatistics, econometrics, quantitative social sciences, program evaluation
