Diplôme de l’Ecole polytechnique
MSc of Computational and Mathematical Engineering – Stanford University
Study of causal networks.
Causal inference is very important to a wide range of use from clinical trials to econometrics: we learned that “correlation is not causation” but how can we learn true causal relationships? We will using learning of causal diagrams using the latest advances in optimization. We will also study experimental design, given your current knowledge, how to best use your limited resources to gain insightful causal information (e.g., by doing biological experimentations)?