Temporal Eligibility Routing (TEAR) for Treatment-Effect Estimation under Structured Covariate Contamination

Temporal provenance and target-relative causal eligibility encode different information in structured covariate exports. Raw inclusion can expose treatment descendants, while temporal deletion can discard delayed records of pretreatment measurements. Temporal Eligibility Routing (TEAR) learns metadata-guided continuous gates and routes feature contributions through a finite-dimensional aggregate before conditional average treatment-effect estimation. A perturbation analysis connects routing-mass errors to representation and prediction perturbations for fixed embeddings and a fitted host. Controlled experiments show that inclusion and deletion favor opposite eligibility regimes, while gated aggregation helps when eligible and ineligible features coexist. Adding TEAR to an otherwise identical preprocessing candidate pool lowers test error measured by precision in estimation of heterogeneous effect (PEHE) in all eight dataset–host cells, with all paired 95% confidence intervals excluding zero. Observed-data selection recovers most of the PEHE-based improvement on MIMIC-P3, while provenance corruption drives gate discrimination toward chance and shifts selection toward Deletion. Code is available in an anonymous repository.