LISER hosted the 2020 Winter School ‘Causal Mediation Analysis and Machine Learning' February 3 & 4
65 PhD students and junior researchers from 11 countries attended a two-day training at LISER
On February 3-4, LISER held the winter school on ‘Causal mediation analysis and machine learning’ which aimed to advance the knowledge and expertise of PhD students as well as junior researchers in the field of causal inference and programme evaluation. With necessary requisites needed to attend the workshop, of the almost 130 applicants, 65 were selected from 11 countries.
The workshop was lead by Professor Martin Huber, Chair of Applied Econometrics/Evaluation of Public Policies at the ‘Department of Economics’ of the University of Fribourg. The format of the training comprised both lectures on various methods of causal mediation analysis and machine learning as well as empirical examples using the statistical software R.
The first part of the two-day training session was focused on mediation analysis, which aims at disentangling a causal effect (e.g. the health effect of education) into different causal mechanisms (e.g. the health effect of education working through increased income). The second part considered machine learning based techniques aiming at leveraging big data with a large number of (control) variables for optimising outcome prediction and causal inference.
This winter school was part of the ’poverty and living conditions pillar’ of InGRID-2, a large research infrastructure aimed at integrating existing research resources in the area of ‘Advanced labour studies. It analyses the future development of work organisation, employer practices and skills analysis’ by organising mutual knowledge exchange activities and improving methods and tools for comparative research on quality of work, skills analysis and evolution of social dialogue. This integration provides the related European scientific community with new and better opportunities to fulfil its key role in the development of evidence-based European policies for Inclusive Growth.