Causal

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Period ending 2026-09-21

29 new papers

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Causal Reasoning

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354 papersExplore

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Covariate Shift

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Causal Foundation Models

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Causal Inferences

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Causal Graph

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Structural Causal Model

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Heterogeneous Treatment Effects

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Latent Confounders

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Treatment

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Propensity

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Causal Representation Learning

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Causal Intervention

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944 papers

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MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
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Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
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What's Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning
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Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
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Multiply Robust Causal Mediation Analysis with Continuous Treatments
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Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
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Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions
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Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
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Oct 25, 2025 · stat.MLJ/K move · Enter open · S save

MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions

Hans Jarett Ong, Yoichi Chikahara, Tomoharu Iwata

Nara Institute of Science and Technology, Nara, Japan · NTT Communication Science Laboratories, Kyoto, Japan

Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions. We introduce MetaCaDI, the first framework to cast the identification of unknown interventions as a meta-learning problem, explicitly leveraging a jointly learned causal graph. MetaCaDI is a Bayesian framework that learns a shared causal structure across multiple environments and is optimized to rapidly adapt to new, few-shot intervention target identification tasks. A key innovation is our model's analytical adaptation, which uses a closed-form solution to bypass expensive and potentially unstable gradient-based bilevel optimization. Extensive experiments on synthetic and complex gene expression data demonstrate that MetaCaDI significantly outperforms state-of-the-art methods. It excels at identifying intervention targets from as few as 3 samples - where existing methods collapse to random chance - while robustly recovering the shared causal graph, proving its effectiveness in data-scarce scenarios.

Causal Foundation Models Causal Graph Meta-Learning Meta Dark Knowledge Unknown Environments

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