Research Group Michael Schomaker
Michael Schomaker
Prof. Dr.
Associate
Michael Schomaker
is Professor for Biostatistics at LMU Munich.
His interests cover a wide range of topics. Currently, the appropriate use of modern causal inference methods is a key aspect of his research: this includes practical considerations for the application of these methods to imperfect longitudinal observational databases, continuous interventions, causal fair machine learning, as well as some more foundational issues.
Publications @MCML
2025
[3]
H. Bao • M. Schomaker
Addressing Positivity Violations in Continuous Interventions through Data-Adaptive Strategies.
Preprint (Feb. 2025). arXiv
Addressing Positivity Violations in Continuous Interventions through Data-Adaptive Strategies.
Preprint (Feb. 2025). arXiv
2024
[2]
L. Bothmann • K. Peters • S. Dandl • M. Schomaker • B. Bischl
Causal Fair Machine Learning.
Statistical Computing 2024 - 54. Arbeitstagung der Arbeitsgruppen Statistical Computing, Klassifikation und Datenanalyse in den Biowissenschaften. Günzburg, Germany, Jul 28-31, 2024. PDF
Causal Fair Machine Learning.
Statistical Computing 2024 - 54. Arbeitstagung der Arbeitsgruppen Statistical Computing, Klassifikation und Datenanalyse in den Biowissenschaften. Günzburg, Germany, Jul 28-31, 2024. PDF
2023
[1]
L. Bothmann • S. Dandl • M. Schomaker
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
ECAI 2023 - 1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. PDF
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
ECAI 2023 - 1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. PDF
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