Removing systematic errors for exoplanet search via latent causes
This addresses the problem of confounders in exoplanet detection for astronomers, but appears incremental as it builds on existing causal inference work.
The paper tackles the problem of removing systematic errors in exoplanet search by reconstructing a latent quantity of interest, using a method called half-sibling regression inspired by causal inference, and demonstrates its potential in a challenging astronomy application.
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.