Jonas Peters: Causality and data

Abstract Causality enters data science in different ways. The goal of causal discovery is to learn causal structure from observational data, an important but difficult problem. Several methods rely on testing for conditional independence. We prove that, statistically, this is fundamentally harder than testing for unconditional independence; solving it requires carefully chosen assumptions on the data generating process. In many practical problems, the focus may lie on prediction, and it is not necessary to solve (full) causal discovery. It might still be beneficial, however, to apply causality related ideas. In particular, interpolating between causality and predictability enables us to infer models that yield more robust prediction with respect to changes in the test set. We illustrate this idea for ODE based systems considering artificial and real data sets. The talk does not require any prior knowledge in causal inference. It contains joint work with Stefan Bauer, Niklas Pfister, and Rajen Shah.