Tabular and Causal Foundation Modelling: TabDPT and CausalPFN

Tabular and Causal Foundation Modelling: TabDPT and CausalPFN

Tabular data powers decision-making across industries, yet its diversity has long limited the reach of deep learning. TabDPT breaks this barrier: it’s a tabular foundation model (TFM), trained using real-world data, that generalizes rapidly to new tasks and domains. TabDPT is the first TFM to demonstrate scaling laws akin to those seen in large language models, predictably improving by increasing the size of the model and training data. TabDPT’s scalability, robust representations, and in-context learning make it the backbone for next-generation applications, including CausalPFN. Built on TabDPT's scalable architecture, CausalPFN automates causal effect estimation from observational data, eliminating manual model selection and tuning. By amortizing learning over a vast array of data generating processes, CausalPFN delivers out-of-the-box causal effect estimation on unseen observational datasets without any fine-tuning or parameter selection, and surpasses the state-of-the-art for models trained on individual datasets. Together, TabDPT and CausalPFN set a new standard for tabular AI, combining broad generalization with trustworthy causal reasoning for real-world impact.