Research interests:
Causal inference; Machine learning; Econometrics; Digitization and data-driven decision-making in marketing, health, and public policy.

Working papers

Seminar presentations:

Poster presentations:

Seminar presentations:     

Invited presentations:        

Publications

Poster presentation:

Media coverage: 



4. Predicting COVID-19 Spread from Large-Scale Mobility Data
    Amray Schwabe, Joel Persson, and Stefan Feuerriegel,
    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining: 3531–3539, 2022
    [working paper version]


Other publications

PhD Thesis

Causal Machine Learning for Data-Driven Decision-Making: Methods and Applications
[link] 

In my doctoral dissertation, I develop and apply methods at the intersection of causal inference, statistical learning and offline reinforcement learning to address empirical problems in digitization. In particular, I study dynamic treatment personalization in digital health, optimal content promotions for online news publishers, and the detection and mitigation of algorithmic bias in heterogeneous treatment effect models deployed on online platforms. To this end, I adapt causal machine learning techniques to address challenges for their use in real-world applications.


Master's Theses