Causal Machine Learning

Predictive machine-learning models primarily capture statistical associations, whereas many scientific and high-stakes applications require reasoning about relationships, interventions, and underlying data-generating mechanisms.

We investigate causal structure learning and related methods for discovering and representing causal relationships from observational data. Our work considers challenges such as complex dynamical systems, incomplete observations, and the integration of causal reasoning with modern machine-learning methods.