Winter school ‘Causal mediation analysis and machine learning’

About

The winter school on ‘Causal mediation analysis and machine learning’ aims at advancing the knowledge and expertise of PhD students as well as junior researchers in the field of causal inference and programme evaluation.

Course leader

LISER

Target group

Requirements for attending the workshop: previous knowledge of basic principles in statistics and econometrics are required at the undergraduate level.

Course aim

The first part of the two-day training session will focus 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 will consider machine learning techniques aiming at leveraging big data with a large number of (control) variables for optimising outcome prediction and causal inference.