معرفی
Michael Knaus is a Junior Professor (Assistant Professor) in the Department of Economics within the Faculty of Economics and Social Sciences at the University of Tübingen, Germany. His office is located at Mohlstraße 36, 4th floor, room 415. He teaches graduate-level courses on causal inference and causal machine learning.
Dr. Knaus specializes in the intersection of causal inference and machine learning, with particular expertise in Double Machine Learning methods. His research focuses on developing advanced statistical techniques to estimate treatment effects across various economic contexts including labor markets, finance, education, and health economics. His work bridges theoretical econometrics with practical applications, emphasizing methodological rigor and real-world relevance.
His recent publications demonstrate a clear progression toward increasingly sophisticated methods for handling heterogeneous treatment effects and complex causal structures. His research shows strong integration of machine learning algorithms with causal inference frameworks to address challenging policy questions across multiple domains.
- Double Machine Learning based Program Evaluation under Unconfoundedness (The Econometrics Journal, 2022)
- Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (Journal of Human Resources, 2022)
- How Does Post-Earnings Announcement Sentiment Affect Firms' Dynamics? (Journal of Financial Econometrics, 2024)
- Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments (2021)
Dr. Knaus has made significant methodological contributions through his development of the causalDML R package, which implements Double Machine Learning methods for binary and multiple treatment effect estimation. His work has been published in top econometrics and economics journals and has gained recognition in the research community, with his GitHub repository accumulating 36 stars. He frequently collaborates with Michael Lechner, a leading researcher in causal inference and program evaluation.
His teaching includes E464 Causal Inference and E463 Causal Machine Learning, both graduate courses that combine theoretical foundations with practical implementation using R. These courses prepare students for advanced research and data science roles requiring sophisticated causal reasoning skills, emphasizing hands-on application of methods to real-world problems.
