Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity

• URL: http://arxiv.org/abs/2607.10540v1
• Date: Sun, 12 Jul 2026 02:52:14 GMT
• Title: Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity
• Abstract summary: We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation.<n>We show that more accurate first-stage representation learning can yield a more informative plug-in approximation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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