Optimal Transport for Structure Learning Under Missing Data

arXiv:2402.15255v1 Announce Type: new
Abstract: Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or preferably causal relations among variables. Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirical shown to be sub-optimal. To this end, we propose in this paper a score-based algorithm, based on optimal transport, for learning …

abstract arxiv chicken cs.ai cs.lg data dependencies discovery imputation missing values relations robust transport true type values variables