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Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models.

, , , и . NeurIPS, (2020)

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Partial identification of dose responses with hidden confounders., , , , , и . UAI, том 216 из Proceedings of Machine Learning Research, стр. 1368-1379. PMLR, (2023)Task Adaptive Metric Space for Medium-Shot Medical Image Classification., , , , и . MICCAI (1), том 11764 из Lecture Notes in Computer Science, стр. 147-155. Springer, (2019)Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data., , , , , и . NeurIPS, стр. 30465-30478. (2021)Differentiable Multi-Target Causal Bayesian Experimental Design., , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 34263-34279. PMLR, (2023)CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance., , , , , и . MICCAI (3), том 10435 из Lecture Notes in Computer Science, стр. 639-646. Springer, (2017)Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models., , , и . NeurIPS, (2020)GeneDisco: A Benchmark for Experimental Design in Drug Discovery., , , , , , и . ICLR, OpenReview.net, (2022)Estimating the Hallucination Rate of Generative AI., , , , , , , и . CoRR, (2024)Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions., , , , , , , и . NeurIPS, (2022)Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 4829-4838. PMLR, (2021)