Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation

ArXi:2605.06385v1 Announce Type: new Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and feedback loops are present. Existing approaches typically assume acyclicity or rely on global causal structure learning, limiting applicability and computational efficiency. In this work, we study a local, data-driven method for covariate selection based on conditional independence information.