Article

TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID

2025-10-05

Abstract excerpt

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 hype...

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Literature Corpus work: b906ef85-1741-5dca-a78a-afd493b51278
DOI: 10.1101/2025.10.02.25337138

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