Causality at the Intersection of Simulation, Inference, Science, and Learning
The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems.
