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

The confluence of access to large granular data sources (‘Big Data’) and the rapid advance of modelling techniques like those from machine learning (ML) promises new insights into the economy and a larger information set for policymakers.

The Bank of England (BoE) and the Data Analytics for Finance and Macro (DAFM) Research Centre at King's Business School at King’s College London have recently initiated a series of annual scientific conferences to discuss these advances. 

Our events aim both to discuss recent developments and, crucially, focus on particular aspects of big data and ML methodology which are of increased interest to applied researchers. Two such aspects 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 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.

A focus on interpretability of, and uncertainty around, modelling outputs is particularly welcome. The conference aims to provide an opportunity to discuss recent scientific advances, especially with a focus on problematics expounded above, as well as to connect policy makers and academics. 

We will consider submissions covering a wide range of topics including:

• Large granular structured or unstructured data sources, e.g. administrative data, web data, from the “digital exhaust”, text data
• Machine Learning for prediction and understanding the economy and its interpretation
• Interpretability and uncertainty measurement of non-parametric methods, e.g. ML
• Data methods, e.g. matching, filtering or cleaning techniques. 
• Theory, e.g. estimation with many covariates or strong non-linearities, model and estimation uncertainty of ML approaches
  
  Currently confirmed keynote speakers:
• Victor Chernozhukov (Massachusetts Institute of Technology)
• Francesca Toni (Imperial College London)

The event is free of charge for all participants.

Important Dates:

• 4 August 2019: Submission deadline
• 8 September 2019: Author notification
• 20 October 2019: Submission of final version of accepted papers
• 20 October 2019: Registration deadline
• 4 – 5 November 2019: Conference

Scientific Committee:

• Andrew Blake (Bank of England (BoE))
• Mingli Chen (University of Warwick)
• Stephen Hansen (University of Oxford)
• Andreas Joseph (BoE and Data Analytics for Macro & Finance (DAFM) Research Centre at King's Business School)
• George Kapetanios (Committee Chair, King's Business School; DAFM)
• Christopher Kurz (Federal Reserve Board)
• Fotis Papailias (King’s Business School, DAFM)
• Chris Redl (BoE and DAFM)

Local Organising Committee:

Andrew Blake (BoE)

Andreas Joseph (BoE and DAFM)

George Kapetanios (King’s Business School, DAFM)

Fotis Papailias (King’s Business School, DAFM)

This conference is jointly partnered by the Data Analytics for Finance and Macro (DAFM) Research Centre at King's Business School and the Bank of England.