Abstract

Why are we interested in the causal structure of a data-generating process? In a classical regression problem, for example, we include a variable into the model if it improves the prediction; it seems that no causal knowledge is required. In many situations, however, we are interested in the system’s behaviour under a change of environment. Here, causal models become important because they are usually considered invariant under those changes. A causal prediction (which uses only direct causes of the target variable as predictors) remains valid even if we intervene on predictor variables or change the whole experimental setting. We introduce structural equation models as a way of formalizing the invariance principle described above and present two ideas that can be used to infer causal structure from data: (1) restricted structural equation models and (2) a recent method that exploits the invariance principle when data from different environments are available. This talk is meant as a short tutorial. It concentrates on ideas and concepts and does not require any prior knowledge about causality.