Vanessa Didelez
Papers
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About
Vanessa Didelez is a leading figure in causal inference and biostatistics, renowned for her foundational work on graphical models and causal reasoning in complex observational studies. Her research spans causal discovery, instrumental variables, and the integration of causal thinking into epidemiology and artificial intelligence. Didelez made major contributions by developing methods for identifying causal effects from longitudinal and time-to-event data, particularly through the use of directed acyclic graphs and structural equation models. She is widely cited for her work on the "front-door criterion" and for clarifying the role of instrumental variables in non-experimental settings, with her most influential papers accumulating hundreds of citations. Notably, her recent foray into robotics—exploring probabilistic actual causation to improve autonomous pouring tasks—showcases her ability to bridge theoretical causality with real-world applications. Didelez’s achievements include leading interdisciplinary projects and serving as an editor for top journals, cementing her reputation as a thinker who makes causal inference accessible and actionable across fields. Her work continues to inspire researchers seeking rigorous tools for understanding cause and effect.
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