Economic data science

How can we use modern computational and statistical methods to analyse economic data?




The goal of this interest group is to provide a platform for economics and econometrics research at the Turing, and to foster collaborations and exchange between economics, statistics, and computer science. Those fields share a strong joint interest in artificial intelligence and machine learning methods, and in the application of those methods to topics like causal inference, policy evaluation, welfare analysis, social networks, language processing, spatial mobility, and high-dimensional inference, to name just a few. We will organize reading groups, informal workshops, seminars, and conferences on such topics at the intersection between economics and data science. 


We want to encourage economists to come to the Turing on a regular basis, and to interact and engage with the other fields that are already strongly represented here. There is a large potential for collaborations on joint research projects and funding applications that will be stimulated by the regular activities of the interest group.

Talking points

Addressing causal inference problems in economics applications.

Policy evaluation and design with heterogeneous populations.

Applications of reinforcement learning and agent-based modelling.

Addressing normative questions of AI from the perspective of causal impact and social welfare.

Inference methods for social network data.

Production networks and supply chains.

Analysis of large-scale transaction data both on consumer and firm-to-firm data.

Analysis of high frequency national account data.

Natural language processing applications in economics.

How to get involved

Click here to join us and request sign-up



Contact info

Mingli Chen
[email protected]

Martin Weidner
[email protected]