Apologies for cross-posting ******************************* *CALL FOR PAPERS -- **WHY-21 @ NeurIPS* *Workshop on "Causal Inference & Machine Learning: Why now?" *
Dear all, We invite you to submit your paper to the WHY-21 Workshop - Causal Inference & Machine Learning: Why now? <https://why21.causalai.net/>, to be held virtually as part of the NeurIPS 2021 <https://neurips.cc/Conferences/2021/Schedule?showEvent=21871> on December 13th, 2021. This 2nd edition of the WHY workshop (1st edition: WHY-19 <https://why19.causalai.net/>) focuses on bringing together researchers from both ML & Causality to initiate principled discussions about the integration of causal reasoning and machine learning perspectives to help tackle the challenging AI tasks of the coming decades. We welcome researchers from all relevant disciplines, including but not limited to computer science, cognitive science, robotics, mathematics, statistics, physics, and philosophy. We welcome paper submissions, including ongoing and more speculative work. Preference will be giving to unexplored tasks and new connections with the current paradigm in the field. The overall goal is to have a forum for open discussion instead of only being an alternative venue for already mature work. We will consider papers that describe methods for (1) answering causal questions with the help of ML machinery, or (2) methods for enhancing ML robustness and generalizability with the help of causal models (i.e., carriers of transparent structural assumptions). Authors are encouraged to identify the specific task the paper aims to solve and where on the causal hierarchy their contributions reside (i.e., associational, interventional, or counterfactual). Submission website: https://cmt3.research.microsoft.com/WHY2021/ All accepted papers will be presented as posters during the workshop, and some of them will be selected for oral presentations. For more details, check the workshop webpage: https://why21.causalai.net/ *Topics of interest (not limited to):* - Algorithms for causal inference and mechanisms discovery. - Causal analysis of biases in data science & fairness analysis. - Causal and counterfactual explanations. - Generalizability, transportability, and out-of-distribution generalization. - Causal reinforcement learning, planning, and imitation. - Causal representation learning and invariant representations. - Intersection of causal inference and neural networks. - Fundamental limits of learning and inference, the causal hierarchy. - Applications of the 3-layer hierarchy (Pearl Causal Hierarchy). - Evaluation of causal ML methods (accuracy, scalability, etc). - Causal reasoning and discovery in child development. - Other connections between ML, cognition, and causality. *Important Dates: * - Paper submission deadline: September 18th, 2021, AoE - Notifications of acceptance: October 23nd, 2021, AoE - Conference date: December 13th, 2021 *Organizers: * - Elias Barienboim, Columbia University - Bernhard Scholkopf, Max Planck Institute - Terry Sejnowski, Salk Institute & UCSD - Yoshua Bengio, U Montreal & Mila - Judea Pearl, UCLA
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