Methods and machine learning

MiSoC’s methodology work covers the following areas: the use of machine learning techniques from computer science for the analysis of social science data; the use of genomic, epigenetic and proteomic data to help understand the relationship between education and mental health; and the use of genomic databases to understand the causes of historical demographic trends.

Research area leader: Paul Clarke
Paul Clarke

MiSoC’s new programme of methodological work brings data science into the MiSoC programme across all research areas, and deals with crucial measurement issues key to our research programme. Our machine learning projects involve applying machine learning techniques a) to reduce the sorts of bias one can find when using conventional causal techniques (e.g. propensity scores, instrumental variables regression), and b) to identify groups of people who are affected in very different ways by treatments or social exposures.

Research team

  • Sule Alan
    Professor of Economics, European University Institute
  • Nicola Barban
    Professor of Demography, University of Bologna, Italy (Co-I)
  • Paul Clarke
    Professor of Social Statistics, University of Essex (Co-I)
  • Anna Dearman
    Senior Reserach Officer, University of Essex
  • Adeline Delavande
    Professor of Economics, University of Technology, Sydney (Co-I)
  • Meena Kumari
    Professor of Biological and Social Epidemiology, University of Essex (Co-I)
  • Annalivia Polselli
    Postgraduate Research Student, Department of Economics, University of Essex
  • Spyros Samothrakis
    Lecturer, Computer Science & Electronic Engineering, University of Essex (Co-I)


ESRC Research Centre on Micro-Social Change (MiSoC)
Institute for Social and Economic Research
University of Essex
Wivenhoe Park

Follow us on Twitter: @MiSoC_Essex