Instrumental Variable Methoden für Sequentielles Experiment-Design
Abstract
This thesis develops instrumental variable (IV) techniques for sequential experimental design to quantify causal relationships in high-dimensional systems with hidden confounders. Motivated by challenges in biological datasets—such as high dimensionality, hidden confounding, nonlinear effects, and limited resources—we address these issues by extending IV methods to underspecified linear and nonlinear settings, refining bounds for causal effects, and optimizing experiment selection.
Fields of Study & Subjects
Advanced Causal Inference Techniques
Optimal Experimental Design Methods
Statistical Methods and Inference
Publication details
- Journal / source
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)
- Publication date
- 13 January 2026
- Language
- EN
- Document type
- Thesis