Logic-based Integrative Causal Discovery
Causal modeling is important in biomedicine because it describes a system’s behavior not only under observation but also under intervention. Logic-based causal discovery exploits this expressive power to identify causal models from data sets that may be obtained under different experimental conditions and measure different variables. This approach is shown to produce non-trivial predictions in public data sets from a wide range of scientific domains. The talk will also discuss possible applications of causal discovery in biomedicine.