Causal Learning through Deliberate Undersampling.
Kseniya P. Solovyeva, David J. Danks, Mohammadsajad Abavisani, Sergey Plis · PubMed · 2023
than our current instruments. We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.