arXiv stat.ML
· Papers
Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity
arXiv:2607.10540v1 Announce Type: new 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