Discovering Invariant and Changing Mechanisms from Data

Sarah Mameche, David Kaltenpoth, Jilles Vreeken

Abstract: While invariance of causal mechanisms has inspired recent work in both robust machine learning and causal inference, causal mechanisms may also vary over domains due to, for example, population-specific differences, the context of data collection, or intervention. To discover invariant and changing mechanisms from data, we propose extending the algorithmic model for causation to mechanism changes and instantiating it using Minimum Description Length. In essence, for a continuous variable Y in multiple contexts C, we identify variables X as causal if the regression functions g : X → Y have succinct descriptions in all contexts. In empirical evaluations we show that our method, VARIO, finds invariant variable sets, reveals mechanism changes, and discovers causal networks, such as on real-world data that gives insight into the signaling pathways in human immune cells.