CausaBridge: An Identification-Gated Causal Foundation Model for Transferable Effect Estimation

Foundation models amortize inference across tasks, but causal effect estimation has a prerequisite absent from ordinary prediction: the requested effect must be identifiable under the declared causal model and available distributions before a numerical estimator is invoked. We introduce CausaBridge, an identification-gated causal foundation model. A typed task contract is compiled into a candidate identification certificate and independently verified; only an accepted point certificate can execute the contract-conditioned estimator. Verified bounds bypass the neural path, while non-identified, unsupported, or empirically unsupported queries return typed non-point results. We establish point-output soundness, noninterference of non-point branches, stability of registered functionals to nuisance error, conditional invariance under kernel-preserving adapters, and a task-distribution generalization bound. A prespecified common-target study evaluates 18 checkpoints on 256 unseen tasks (4,608 evaluations); diverse-mechanism pretraining reduces calibrated ATE error by 0.0180 relative to fixed-mechanism pretraining (95% crossed-bootstrap CI: 0.0117–0.0242; $p<10^{-4}$) and satisfies a four-part decision rule specified before confirmatory evaluation. A 5,376-job comparison study and a matched 312-job selective evaluation on IHDP, ACIC, Twins, and Jobs produce 309 authorized point results and three typed refusals. On supported ACIC repetitions, CausaBridge reduces ATE error relative to CausalPFN by 0.0741 (95% repetition-bootstrap CI: 0.0419–0.1112). On nine authorized MIMIC-based semi-synthetic scenarios, CausaBridge obtains 0.1353 ATE MAE and 0.1756 root precision in estimation of heterogeneous effect (PEHE) across two independent seeds; adapter-only transfer updates 5.8% of parameters and reduces both errors relative to full fine-tuning under the prespecified protocol. In five-seed structural replay, the complete executor produces no false point outputs and makes no neural calls on non-point branches. A linked real-cohort audit separately converts inadequate treatment support into a typed non-point outcome. CausaBridge thereby unifies exact-query authorization, typed non-point execution, and reusable causal effect estimation in a single verified pipeline.