The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
De- mystifying amortized causal discovery with transformers.arXiv preprint arXiv:2405.16924, 2024
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cs.LG 3years
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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.
TabCausal is a causal discovery foundation model pretrained across diverse synthetic causal environments that reports better macro-averaged performance than baselines on both synthetic and LLM-audited semantic benchmarks.
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Computational Identifiability
The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
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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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TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
TabCausal is a causal discovery foundation model pretrained across diverse synthetic causal environments that reports better macro-averaged performance than baselines on both synthetic and LLM-audited semantic benchmarks.