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 induced 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, when more more causal inference has been provided 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 been recently 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 highly technical individuals who who are strongly motivated by the practicality 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.
Bryant Chen, IBM
Panos Toulis, University of Chicago
Alexander Volfovsky, Duke University
We are happy to announce the call for papers (contributed talks and posters) for the 7th UAI Causal Inference Workshop 2018. The workshop will take place on August 10th, 2018 in Monterey, CA After the main conference.
Website:
https://sites.google.com/view/causaluai2018/home
Call for abstracts
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The UAI Causal Workshop welcomes contributions from a variety of perspectives from machine learning, statistics, economics and social sciences, among others. This includes, but it is not limited to, the following topics:
- Causal discovery and relational learning
- Estimation of causal graphs and related tasks
- Measures and methods for evaluating the quality of causal predictions
- Combining experimental control and observational data
- Interactive experimental control vs. counterfactual estimation from logged experiments
- Bandit algorithms and reinforcement learning
- Discriminative learning vs. generative modeling in counterfactual settings
- Handling selection bias
- Identification and estimating causal effects from observational data
- Deriving testable implications of causal models
- Applications in online systems (e.g. search, recommendation, ad placement)
- Applications in complex systems (e.g. cell biology, smart cities, computational social sciences)
- Applications in economics
At the discretion of the organizers, some contributions will be assigned slots as short contributed talks and others will be presented as posters.
We suggest extended abstracts of 2 pages in the UAI format, but no specific format is enforced. A maximum of 6 pages will be considered. PDF files only.
Submission page: https://easychair.org/conferences/?conf=causaluai2018
08月07日
2018
08月10日
2018
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