Disentangled Double Machine Learning for Accurate Causal Effect Estimation

ArXi:2605.24808v1 Announce Type: cross Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios.