{"ID":6536106,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T01:50:16.627184149Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10540","arxiv_id":"2607.10540","title":"Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity","abstract":"We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the \"no essential heterogeneity\" (NEH) assumption. We call the method UNIT. In the first stage,TARNet estimates the heterogeneous effect of a randomized treatment on a mediator by learning a shared covariate representation across treatment arms.The resulting conditional average treatment effect (CATE) estimate provides a plug-in approximation to the heterogeneity-dependent component of the weight function entering the G-estimating equation of Zheng and Zhou (2015), which identifies the structural parameters even in the presence of unmeasured mediator-outcome confounding. We show that more accurate first-stage representation learning can yield a more informative plug-in weight and thereby improve the precision of the structural parameter estimator. In simulations with non-Gaussian covariates and nonlinear mediator effects, TARNet weights reduce the Stage-2 standard error of the mediation coefficient by a factor of $1.45$ to $1.51$ (median across replications, $n \\ge 2000$) relative to the classical approach, at no cost to bias or coverage.","short_abstract":"We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the \"no essential heterogeneity\" (NEH) assumption. We call the method UNIT. In the first stage,TARNet estimates the heterogeneous effect of a randomized treatment on a mediator by lear...","url_abs":"https://arxiv.org/abs/2607.10540","url_pdf":"https://arxiv.org/pdf/2607.10540v1","authors":"[\"Roberto Faleh\",\"Sofia Morelli\",\"Holger Brandt\"]","published":"2026-07-12T02:52:14Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[]","has_code":false}