# The causal structure of galactic astrophysics

arXiv: https://arxiv.org/abs/2510.01112 · HTML: https://mlsanity.com/paper/2510.01112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing](https://mlsanity.com/paper/2510.04263/) (2025)
- [Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests](https://mlsanity.com/paper/2510.04276/) (2025)

## Abstract (arXiv)

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. We develop a causal discovery algorithm suitable for large astrophysical datasets and illustrate it on ~4.5×105 nearby galaxies from the Nasa Sloan Atlas, demonstrating its ability to distinguish physical mechanisms that are degenerate on the basis of correlations alone.