CORDIS Project
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This project aims to develop a new statistical modeling framework that enhances data analysis quality by minimizing bias and maximizing interpretability. It focuses on causal inference and provides methods applicable across various empirical sciences, addressing challenges in causal modeling.
I propose a cutting-edge and transformative paradigm for statistical modelling that is crucial to enhance the quality of data analyses.
Leveraging my expertise in causal inference and semiparametric statistics, I will establish the fundamental principles of a comprehensive estimation theory, which maps model parameters onto generic, interpretable, model-free estimands (e.g., association or effect measures) with favourable efficiency bound, and harnesses the power of debiased (statistical/machine…
UNIVERSITEIT GENT
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