Overview
The NeurIPS 2019 workshop on Causal Machine Learning invites researchers to submit papers exploring various aspects of causal inference and related fields. This workshop is significant as it aims to bridge disciplines and foster collaboration among researchers from diverse backgrounds.
Background & Relevance
Causal Machine Learning is a rapidly evolving area that integrates methodologies from statistics, econometrics, and social sciences to improve our understanding of causal relationships. This field is crucial for developing models that can predict outcomes based on interventions, which has applications in areas such as healthcare, economics, and social policy. The workshop will serve as a platform for sharing innovative research and methodologies that advance this important domain.
Key Details
- Event: NeurIPS 2019 Workshop on Causal Machine Learning
- Location: Vancouver
- Dates: December 13 or 14, 2019 (exact date to be confirmed)
- Submission Deadline: September 9, 2019
- Acceptance Notification: October 1, 2019
- Link for Details: NeurIPS 2019 Causal ML Workshop
Eligibility & Participation
This workshop is open to researchers from various disciplines, including statistics, biostatistics, econometrics, and the quantitative social sciences. It targets both established researchers and newcomers who are working on causal inference and related methodologies.
Submission or Application Guidelines
Interested participants should prepare and submit their manuscripts by the specified deadline. Extended abstracts are encouraged to facilitate the dissemination of work intended for journal submission. Detailed submission guidelines can be found on the workshop’s official webpage.
Additional Context / Real-World Relevance
Understanding causal relationships is vital for making informed decisions in many fields. The insights gained from this workshop will contribute to the development of more robust models that can effectively predict outcomes based on interventions, which is essential for policy-making and scientific research.
Conclusion
Researchers are encouraged to explore this opportunity to contribute to the growing field of Causal Machine Learning. By submitting your work, you can engage with peers and share your findings at a prominent venue. We look forward to your submissions and to welcoming you to Vancouver in December 2019.
Category: CFP & Deadlines
Tags: causal inference, machine learning, neuroscience, statistics, biostatistics, econometrics, quantitative social sciences, program evaluation
