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Paul Hünermund
Has grant
Professor of Empirical Economics & Data Science
Research Interests
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Recent Grants
Grant: Open
Optimizing Human-AI Interaction: Integrating Domain Knowledge into Causal AI Systems
Open Date: 2026-04-01
Close Date: 2029-12-01
Positions (1)
Paul Hünermund
Technical University of Munich
5 months ago
Technische Universität München (TUM), Campus Heilbronn, is recruiting for PhD positions and also mentions postdoc opportunities in Empirical Economics, Data Science, Causal AI, Econometrics, Innovation Policy, and Technology Management at the TUM Heilbronn Data Science Center and TUM School of Management. The professorship is led by Paul Hünermund , Professor of Empirical Economics & Data Science. The research environment is interdisciplinary and focuses on rigorous empirical work at the intersection of cause-and-effect analysis, AI, decision-making, organizational design, and firm strategy. Applicants with an interest or background in causal data science are especially encouraged to apply. Eligibility highlights: a Master’s degree in Economics, Data Science, Management, or a related field; strong quantitative and statistical background; programming skills in Python, R, or similar languages; ability to conduct independent research; experience with large datasets is an advantage; excellent English communication skills; and strong teamwork/organizational skills. Funding: PhD positions are initially funded at 75% TV-L E13, with the possibility of increasing to 100%. Application deadline: 30 May 2026. Applicants should submit a single PDF containing a cover letter, CV, academic transcripts, and references if available, by email to [email protected]. The subject line should be “PhD Empirical Economics and Data Science.” Postal applications are also accepted. This opportunity is based at TUM Campus Heilbronn , part of the Technical University of Munich, and offers a collaborative, international, and highly interdisciplinary research setting with opportunities to publish, present, teach, and work with academic and industry partners.
5 months ago
Articles (3)
Year:
2026
Year:
2025
Control variables are included in regression analyses to estimate the causal effect of a treatment on an outcome. In this article, we argue that the estimated effect sizes of controls are unlikely to have a causal interpretation themselves, though. This is because even valid controls are possibly endogenous and represent a combination of several different causal mechanisms operating jointly on the outcome, which is hard to interpret theoretically. Therefore, we recommend refraining from interpreting the marginal effects of controls and focusing on the main variables of interest, for which a plausible identification argument can be established. To prevent erroneous managerial or policy implications, coefficients of control variables should be clearly marked as not having a causal interpretation or omitted from regression tables altogether. Moreover, we advise against using control variable estimates for subsequent theory building and meta-analyses.
Year:
2023
Collaborators (2)
Mikko Rönkkö
Professor of Entrepreneurship
University of Jyväskylä
Beyers Louw
Assistant professor
Erasmus University Rotterdam
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