Overview
The NeurIPS 2019 workshop on Causal Machine Learning is inviting submissions for papers that explore various aspects of causal inference and related fields. This event will take place in Vancouver, providing a platform for researchers to share their findings and insights in a collaborative environment.
Background & Relevance
Causal Machine Learning is an emerging area that combines principles from statistics, economics, and social sciences to understand and predict outcomes based on causal relationships. This workshop aims to foster discussions and advancements in methodologies that can enhance causal inference and counterfactual prediction, which are crucial for informed decision-making in various domains.
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: Causal Machine Learning Workshop
Eligibility & Participation
This workshop is open to researchers from diverse fields, including statistics, biostatistics, econometrics, and the quantitative social sciences. It particularly encourages submissions that contribute novel insights or methodologies relevant to causal inference and its applications.
Submission or Application Guidelines
Interested participants should prepare their manuscripts in accordance with the workshop’s guidelines. Extended abstracts are welcomed, especially those aimed at journal dissemination. Ensure that submissions are made by the specified deadline to be considered for acceptance.
Additional Context / Real-World Relevance
The significance of causal inference extends beyond academia, impacting fields such as public policy, healthcare, and economics. By understanding causal relationships, researchers can develop more effective interventions and policies, making this workshop a vital event for those engaged in these areas.
Conclusion
The NeurIPS 2019 workshop on Causal Machine Learning represents a valuable opportunity for researchers to present their work and engage with peers. Interested individuals are encouraged to submit their papers and participate in this important dialogue within the AI/ML community.
Category: CFP & Deadlines
Tags: causal inference, counterfactual prediction, statistics, biostatistics, econometrics, program evaluation, quantitative social sciences, neurips
