Modelling with Big Data and Machine Learning: Interpretability and Model Uncertainty

04 Nov 2019 09:00AM - 05 Nov 2019 05:00PM
Third party events

The Bank of England (BoE) and the Data Analytics for Finance and Macro (DAFM) Research Centre at King's College London have recently initiated a series of annual scientific conferences to discuss these advances. Two issues form the focus of this two-day conference. The first relates to a commonly cited weakness of ML methods when applied to economic problems and data, which is lack of interpretability of ML model outputs. This makes the adoption of such models difficult for economists who wish to have a more structural understanding of the underlying economic issues. The second, and related, focus is on the estimation and/or calibration of the uncertainty associated with model outputs. Both these matters have not received as much attention in the mainstream ML literature as economists would like it to. We invite you to submit empirical, methodological or theoretical work leveraging on new granular data sources or exploring recent analytical development addressing the above issues and which can be relevant to economic and financial studies or decision making.  Full papers should be submitted to by 4 August 2019.


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