TCPFN is a zero-shot foundation model for temporal causal discovery on panel data that jointly predicts multiple causal aspects and reliability signals, with reported high AUROC on benchmarks and better scaling than PCMCI on large panels.
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Do-pfn: In-context learning for causal effect estimation
Canonical reference. 83% of citing Pith papers cite this work as background.
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FoundCause is a transformer-based amortized model for causal graph discovery that explicitly models latent confounders via learnable tokens and reports better performance than prior methods on 15 real-world datasets.
SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
A prior-data fitted network amortizes causal sensitivity analysis by generating training labels via Lagrangian scalarization, achieving orders-of-magnitude faster bounds computation than per-instance methods.
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.
Oracle Markov boundaries improve prediction on high-dimensional sparse tabular data but causal discovery pipelines rarely recover boundaries that beat using all features.
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Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
A prior-data fitted network amortizes causal sensitivity analysis by generating training labels via Lagrangian scalarization, achieving orders-of-magnitude faster bounds computation than per-instance methods.