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
We are currently working on
- Machine learning in the social sciences
- Measuring subjective expectations
- Covid-19 health risks and ambiguity
Recent Publications
- Gender segregation: analysis across sectoral dominance in the UK labour market -
- Riccardo Leoncini
- Mariele Macaluso
- Annalivia Polselli
- Robustness of algorithms for causal structure learning to hyperparameter choice -
- Damian Machlanski
- Spyridon Samothrakis
- Paul S. Clarke
- Undersmoothing causal estimators with generative trees -
- Damian Machlanski
- Spyridon Samothrakis
- Paul S. Clarke
- Policy uncertainty and information flows: evidence from pension reform expectations -
- Emanuele Ciani
- Adeline Delavande
- Ben Etheridge
- Marco Francesconi
- Estimating mode effects from a sequential mixed-mode experiment using structural moment models -
- Paul S. Clarke
- Yanchun Bao
- Measuring and analyzing protest potential from a survey data recycling framework -
- Joonghyun Kwak
- Random effects dynamic panel models for unequally spaced multivariate categorical repeated measures: an application to child-parent exchanges of support -
- Fiona Steele
- Emily Grundy
- Refractory depression – mechanisms and efficacy of radically open dialectical behaviour therapy (RefraMED): findings of a randomised trial on benefits and harms -
- Thomas R. Lynch
- Roelie J. Hempel
- Ben Whalley
- Sarah Byford
- Rampaul Chamba
- Paul S. Clarke
- Susan Clarke
- David G. Kingdon
- Heather O’Mahen
- Bob Remington
- Sophie C. Rushbrook
- James Shearer
- Maggie Stanton
- Michaela Swales
- Alan Watkins
- Ian T. Russell
- Academic and non-academic investments at university: the role of expectations, preferences and constraints -
- Adeline Delavande
- Emilia Del Bono
- Angus Holford
- A method for decomposing the impact of reforms on the long-run income distribution, with an application to universal credit -
- Mike Brewer
- Robert Joyce
- Tom Waters
- Joseph Woods
Explainers
- What should we do about inter-survey variability in survey data harmonisation? A survey data recycling approach -
- Joonghyun Kwak
- University access: the role of background and Covid-19 throughout the application process -
- Emilia Del Bono
- Laura Fumagalli
- Angus Holford
- Birgitta Rabe
Blog
- Understanding the use of genetics in social science research -
- Anna Dearman
- Designing more robust experiments to improve education research -
- Joshua Fullard
- The value of keeping ethnic ties: why adherence to recommended COVID-19 health behaviours differs among young adults -
- Renee Luthra, Angus Holford, Adeline Delavande
- Are there ethnic differences in adherence to recommended health behaviours related to Covid-19? -
- Renee Luthra
- Hard hit: Covid-19 has changed many lives, but ethnic minorities and women, especially lone mothers, have been particularly affected. -
- Emilia Del Bono
- Income inequality and the very rich -
- Mike Brewer
- Subjective Expectations: Measurement and analysis of decision-making under uncertainty -
- Adeline Delavande
- UKMOD: a new, free-to-use, tax-and-benefit microsimulation model for the UK -
- Mike Brewer
Team
Machine Learning software
Contact
ESRC Research Centre on Micro-Social Change (MiSoC)
Institute for Social and Economic Research
University of Essex
Wivenhoe Park
Colchester
Essex
CO4 3SQ
UK
Email: brabe@essex.ac.uk
Follow us on Twitter: @MiSoC_Essex @rabe_b