Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity
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-outc...
| Authors | Roberto Faleh, Sofia Morelli, Holger Brandt |
|---|---|
| Primary category | stat.ML |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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