TACO: TabPFN augmented causal outcomes for early detection of long COVID

Abstract Long COVID affects 10-40% of COVID-19 survivors, yet early detection remains challenging. We present TACO (TabPFN Augmented Causal Outcomes), a framework that uniquely combines causal inference with foundation models for presymptomatic Long COVID detection. TACO employs Differential Causal Effect (DCE) analysis to identify causally relevant genes, then utilizes TabPFN, a foundation model that does not require hyperparameter adjustment, to achieve consistent performance. In comprehensive benchmarking, TACO achieved superior precision using 18% fewer features than conventional approaches. Critically, TACO maintains consistent performance without any hyperparameter optimization, while benchmark models show variable results depending on the tuning. The causal genes of the framework provide biological interpretability, with 23.6% validated in the Long COVID literature (4.72-fold enrichment, p = 5.04 × 10 −39 ), including regulators of viral entry ( AR, TMPRSS2 ), immune response ( TP53, CDKN1A ), and tissue remodeling ( SMAD2/3 ). By prioritizing causal mechanisms over statistical associations and eliminating the need for hyperparameter search, TACO offers a practical, interpretable solution for clinical deployment, transforming Long COVID management from reactive diagnosis to proactive prevention.