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Research questionHow can we estimate causal effects on new datasets without rebuilding a bespoke inference pipeline?Each intervention question may require its own causal mechanism, estimator, and training process, making reuse across datasets difficult.
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Machine Learning
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Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Causal Foundation ModelsThe source concerns causal foundation models: pretrained neural networks that use in-context learning to estimate quantities such as average treatment effects on entirely new datasets without model updates. It provides causal-inference and machine-learning background with example code and Jupyter notebooks, but does not establish performance or applicability for particular domains.research paper · Sep 2, 2026
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How can complex causal queries be identified under non-IID data, transfer, and missing observations?How can causal explanations scale to individual outcomes without abandoning counterfactual causal structure?How can extreme quantile treatment effects be estimated under heavy tails while respecting location invariance?How can differentiable causal discovery override wrong edge priors while preserving directional identifiability?
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