The Use of Machine Learning Techniques When Dealing with Missing Data |
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| Coordinator 1 | Dr Barbara Felderer (GESIS) |
| Coordinator 2 | Dr Christian Bruch (GESIS) |
Machine Learning methods became very popular in survey research in recent years because of their ability to deal with large data sets and dependencies between variables and their flexibility in modeling complex relationships. While machine learning is optimized for prediction tasks, explanation might be improved using causal machine learning. While both approaches have frequently been used to model social behavior, their use for typical survey methodological and survey statistical applications such as analyses based on missing data is yet rather rare.
Concerning missing data one can think of at least three areas of application:
1. Understanding nonresponse behaviour (e.g., in the context of nonresponse bias analyses) and development of targeted recruitment designs
2. Modelling unit nonresponse and generate adjustment weights
3. Modelling item nonresponse when applying imputation methods
We invite papers addressing the potential of machine learning methods to deal with any kind of missing data. Contributions from fields beyond social science that tackle the challenges of nonresponse are also welcome.