Michael C. Knaus

Michael C. Knaus serves as Assistant Professor of "Data Science in Economics" at the School of Business and Economics, University of Tübingen. He is actively involved in the "Machine Learning for Science" Cluster of Excellence, which recently secured continued funding from the German Research Foundation for seven additional years. His academic work bridges theoretical econometrics with practical machine learning applications, focusing on empirical labor economics research.

Dr. Knaus's research centers on the intersection of causal inference and machine learning, with particular emphasis on identification, interpretation, and estimation of average and heterogeneous treatment effects. His methodological contributions include developing transparent weighting frameworks for causal estimators and improving covariate balancing procedures. He investigates why causal machine learning methods succeed or fail in real-world applications, such as explaining cases where algorithms cannot detect treatment effects in noiseless outcomes.

His recent publications reveal a strong trend toward making causal machine learning more accessible and interpretable for economists. Through arXiv working papers, R packages, and teaching materials, he emphasizes practical implementation of methods like Double Machine Learning while maintaining rigorous theoretical foundations. His work provides essential tools for evaluating when causal ML approaches are appropriate for policy-relevant economic questions.

Dr. Knaus demonstrates strong commitment to academic community building through his Causal ML Workshop for Ukraine, GitHub-hosted teaching materials, and active scholarly discourse on Bluesky. He regularly shares methodological insights, reproduces seminal papers, and promotes collaborative learning in causal inference techniques across international boundaries.