Advances in computational power and statistical algorithms, in conjunction with the increasing availability of large datasets, have led to a Cambrian explosion of machine learning (ML) methods. For population researchers, these methods are useful not only for predicting population dynamics but also as tools to improve causal inference tasks. However, the rapid evolution of this literature, coupled with terminological disparities from conventional approaches, renders these methods enigmatic and arduous for many population researchers to grasp.
This workshop on November 5 to 6, 2024 at the Max Planck Intsitute for Demographic Research (MPIDR) in Rostock, Germany, clarifies the goals, techniques, and applications of machine learning methods for population research. The workshop covers
- an introduction to ML methods for population researchers,
- showcases of ML applications to answer causal questions,
- discussions of the current developments of ML for population health, fertility and family dynamics, and
- fosters critical discussions about the shortfalls of these techniques.
The main focus of this workshop is on ML techniques using quantitative population data and research questions, not on ML language models. The workshop consists of keynotes, contributed sessions, and a tutorial.
More at: www.demogr.mpg.de
Complexity Digest 