David Strieder

M.Sc. David Strieder is a researcher at the Technical University of Munich (TUM), affiliated with the Chair of Mathematical Statistics led by Prof. Drton. His work focuses on causal inference, particularly addressing structure uncertainty in linear causal models. He develops methodologies to rigorously account for uncertainties in causal discovery through frameworks like dual likelihood and test inversion. His research integrates statistical theory with machine learning to improve confidence in causal effect estimation.

David's academic activities include contributions to the TUM's Department of Mathematics, where he collaborates on projects related to causal structure learning and uncertainty quantification. His publications address foundational challenges in causal discovery, including the combination of structure learning with effect inference. He maintains an active presence in academic conferences, presenting at events such as the Conference on Uncertainty in Artificial Intelligence and the Conference on Causal Learning and Reasoning.

Key contributions include advancing dual likelihood approaches for causal inference under structural ambiguity and refining statistical testing techniques for linear models. His work emphasizes practical implementations that balance theoretical rigor with algorithmic efficiency.