Mediation analysis stops refitting per table: MediPFN learns interval error quantiles in advance and runs 59.1 times faster than bootstrap in paired timing
Causal mediation analysis splits a treatment effect into an indirect pathway through mediators and a direct pathway through other processes, and attaching a confidence interval to the indirect effect usually means refitting several nuisance functions on every table and resampling repeatedly. MediPFN adapts a pretrained tabular foundation model to predict those nuisance functions, then a separate head learns quantiles of the estimator's standardized error from simulated tables with known indirect effects, with symmetrization and independent calibration producing the interval. On synthetic, distribution-shift and real-covariate tables its coverage sits closer to the 95% nominal level than several comparators, and paired timing gives a median 59.1-fold speedup over bootstrap.