7th Causal Inference Workshop at UAI
A UAI 2018 Workshop
Intercontinental, Monterey, CA
August 10, 2018
In recent years, causal inference has seen important advances, especially through a dramatic expansion in its theoretical and practical domains. By assuming a central role in decision making, causal inference has attracted interest from computer science, statistics, and machine learning, each field contributing a fresh and unique perspective.
More specifically, computer science has focused on the algorithmic understanding of causality, and general conditions under which causal structures may be inferred. Machine learning methods have focused on high-dimensional models and non-parametric methods, whereas more classical causal inference has been guiding policy in complex domains involving economics, social and health sciences, and business. Through such advances a powerful cross-pollination has emerged as a new set of methodologies promising to deliver robust data analysis than each field could individually -- some examples include concepts such as doubly-robust methods, targeted learning, double machine learning, causal trees, all of which have recently been introduced.
This workshop is aimed at facilitating more interactions between researchers in machine learning, statistics, and computer science working on questions of causal inference. In particular, it is an opportunity to bring together highly technical individuals who are strongly motivated by the practical importance and real-world impact of their work. Cultivating such interactions will lead to the development of theory, methodology, and - most importantly - practical tools, that better target causal questions across different domains.
May 20 -- Paper submission deadline; submission page: https://easychair.org/conferences/?conf=causaluai2018 June 20 -- Author notification
July 20 -- Camera ready version
August 10 -- Workshop
Bryant Chen, IBM
Panos Toulis, University of Chicago
Alexander Volfovsky, Duke University
Junior researcher attendance partially supported by NSF DMS Statistics award 1832831.