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Causal Discovery

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many …
Causal Learning and Reasoning (CLeaR),  2025.
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Learning to Defer for Causal Discovery with Imperfect Experts
Learning to Defer for Causal Discovery with Imperfect Experts
Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge …
Workshop at the International Conference of Learning Representation (ICLR),  2025.
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The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many …
Workshop at the International Conference of Learning Representation (ICLR),  2025.
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A Sparsity Principle for Partially Observable Causal Representation Learning
A Sparsity Principle for Partially Observable Causal Representation Learning
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all …
International Conference on Machine Learning (ICML),  2024.
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Invariant Causal Set Covering Machines
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …
Workshop on Spurious Correlations, Invariance, and Stability (ICML),  2023.
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Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widely recognized, with causal discovery and inference being inherently …
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),  2023.
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Causal Discovery with Language Models as Imperfect Experts
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, …
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),  2023.
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DAG Learning on the Permutahedron
DAG Learning on the Permutahedron

We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our …

International Conference of Learning Representations (ICLR),  2023.
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Can large language models build causal graphs?
Can large language models build causal graphs? 
Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Learning Discrete Directed Acyclic Graphs via Backpropagation
Learning Discrete Directed Acyclic Graphs via Backpropagation
Recently continuous relaxations have been proposed in order to learn directed acyclic graphs (DAGs) by backpropagation, instead of …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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