Chair for Statistics and Data Science in Social Sciences and the Humanities (SODA)
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Write your Thesis with us!

We are always looking for motivated students who are interested in writing about a topic connected to our current research projects!

Potential topics for the summer term 2022

Dynamic Fairness and Algorithmic Decision-Making (Master level)

Public agencies are increasingly automating the allocation of scarce public resources by making use of risk prediction models. While a wide range of studies focuses on bias in the application of such models, the long-term fairness implications of algorithmically assisted decisions are not fully understood. Building on the emerging literature of dynamic fairness, this project aims at studying feedback loops and the long-term consequences of algorithmic decision-making in social contexts. If you are interested, please contact  c.kern@uni-mannheim.de and cc anna-carolina.haensch@stat.uni-muenchen.de with your CV and a short explanation of why you are interested in this topic. In addition, please describe how familiar you are with the topic.

Policy Learning for Fair and Effective Interventions (Master level)

ML methods are increasingly used in combination with ideas from the causal inference literature to explore heterogeneous treatment effects. Such approaches are useful, for example, for personalizing treatments in medicine or for selecting optimal treatment regimes in the delivery of welfare state measures. While topics such as explainability and transparency have already been studied in the past (see, e.g. policy trees), the connection of the causal learning literature to the fairML literature is still weak. However, it is well known that there are many biases present in data used for developing personalized treatments in medicine or in access to welfare state measures. Therefore, we seek students interested in exploring the connection between causal learning and fairML. If you are interested, please contact c.kern@uni-mannheim.de,r.bach@uni-mannheim.de, and cc anna-carolina.haensch@stat.uni-muenchen.de with your CV and a short explanation of why you are interested in this topic. In addition, please describe how familiar you are with the topic.

Global COVID-19 Trends and Impact Survey (Bachelor and Master level)

We are actively looking for students who are interested in writing their Theses with data from the Global COVID-19 Trends and Impact Survey. We are also working on different survey weighting approaches.  If you are interested in survey weighting, cross-country research and/or data from survey experiments, please reach out to anna-carolina.haensch@stat.uni-muenchen.de with your CV and a short explanation of why you are interested in this topic. In addition, please describe how familiar you are with the topic. 

Replicate a meta-analysis (Master level)

This meta-analysis of 70 studies (Konrath et al. DOI: 10.1177/1088868310377395) claims that US college students' empathy levels have fallen over time. The decline really picks up in 2000. The authors speculate that the decline is due to social media use. Could the effect be due to changes in survey mode or declines in survey response rates over time? You would replicate the paper and add these methods variables. If you are interested, please contact steph@umd.edu and cc anna-carolina.haensch@stat.uni-muenchen.de with your CV and a short explanation of why you are interested in this topic. In addition, please describe how familiar you are with the topic.