The causal structure of galactic astrophysics

ABSTRACT Data-driven astrophysics currently relies on the detection and characterisation of correlations between objects' properties, which are then used to test physical theories that make predictions for them. This process fails to utilise information in the data that forms a crucial part of the theories' predictions, namely which variables are directly correlated (as opposed to accidentally correlated through others), the directions of these determinations, and the presence or absence of confounders that correlate variables in the dataset but are themselves absent from it. We propose to recover this information through causal discovery, a well-developed methodology for inferring the causal structure of datasets that is however almost entirely unknown to astrophysics. INTRODUCTION Understanding the physical processes that shape galaxies is a central goal of astrophysics. Empirical progress has traditionally relied on identifying correlations between observed properties, which can then be interpreted in light of theoretical models for galaxy formation and used to constrain them. The advent of large surveys and powerful machine learning techniques has greatly expanded our ability to find such statistical associations, uncovering intricate patterns across high-dimensional parameter spaces. However, correlation alone cannot determine causal influences among variables: which properties are actually responsible for determining others, in what direction this influence goes, and whether there exist confounding variables that are not included in the dataset but influence those that are.