causal structure in ML

Today I had the honor on serving on the PhD advising committee of Yan Liang (Princeton), who is designing her PhD project. She is adding causal structure to an autoencoder such that it can separate stellar variability-induced radial-velocity signals from exoplanet-induced signals in extreme precision radial-velocity data. Her method design is novel, and tests suggest that it might work. The committee recommended adding even more causal structure and physics knowledge (more is probably always better, provided that it isn’t incorrect)! As my loyal reader knows, I think this is the frontier for machine learning in the natural sciences: adding causal structure.