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Uncovering the structure of complex data: progresses in machine learning and causal inference


Besserve,  M
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Besserve, M., & Heinze-Deml, C. (2017). Uncovering the structure of complex data: progresses in machine learning and causal inference. Talk presented at Data Learning and Inference (DALI 2017). Tenerife, Spain. 2017-04-18 - 2017-04-20.

Cite as: https://hdl.handle.net/21.11116/0000-0004-950F-4
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance, understanding cause-effect relationships is necessary if one wants to intervene on the system in order to improve its performance. In this context, scientists often need to be able to draw causal interpretations from complex, real-world data. Causal inference and structural equation models provide a rigorous framework to address these questions. However, the validity of these approaches may be challenged by complex structures involving non-stationarity, non-linearity or high-dimensionality. In particular, these properties frequently occur in natural or artificial systems resulting from interactions between many interdependent parts, such as biological or social networks. This workshop will discuss recent progresses in causal inference and related approaches to deal with data of increasing complexity. It aims at bringing together researchers from various fields to discuss the current challenges in estimating mechanisms from real-world data.