TabPFN is a Prior-Data Fitted Network that approximates Bayesian inference for small tabular classification by training a Transformer once on synthetic data drawn from a causal prior, then solves new tasks in a single forward pass without further updates.
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Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.
TTT-SCL dynamically generates test-aligned training sets for supervised causal learning using score-based functions and outperforms prior SCL and traditional causal discovery methods on benchmarks and real data.
Amortized transformer model with conditional fixed-point iterations learns SCM causal mechanisms from data and graphs, matching per-dataset baselines and outperforming in low-data regimes.
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
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Unsupervised Causal Abstractions Discovery
Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.
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Test Time Training for Supervised Causal Learning
TTT-SCL dynamically generates test-aligned training sets for supervised causal learning using score-based functions and outperforms prior SCL and traditional causal discovery methods on benchmarks and real data.
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Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.