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Causality
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Causality
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 …
Philippe Brouillard
,
Chandler Squires
,
Jonas Wahl
,
Konrad P. Kording
,
Karen Sachs
,
Dhanya Sridhar
,
Alexandre Drouin
Causal Learning and Reasoning (CLeaR), 2025.
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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 …
Danru Xu
,
Dingling Yao
,
Sébastien Lachapelle
,
Perouz Taslakian
,
Sara Magliacane
,
Francesco Locatello
,
Julius von Kügelgen
International Conference on Machine Learning (ICML), 2024.
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Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as …
Dingling Yao
,
Danru Xu
,
Perouz Taslakian
,
Sébastien Lachapelle
,
Sara Magliacane
,
Julius von Kügelgen
,
Francesco Locatello
International Conference of Learning Representations (ICLR), 2024.
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Towards Disentangled High-level Causal Explanations in Text
In this work, we propose a causal representation learning framework for learning disentangled and intervenable high-level explanations …
Navita Goyal
,
Hal Daumé III
,
Alexandre Drouin
,
Dhanya Sridhar
Mid-Atlantic Student Colloquium on Speech, Language and Learning, 2024.
Citation
A Sparsity Principle for Partially Observable Causal
Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods …
Danru Xu
,
Dingling Yao
,
Perouz Taslakian
,
Sébastien Lachapelle
,
Julius von Kügelgen
,
Francesco Locatello
,
Sara Magliacane
Workshop at the Neural Information Processing Systems (NeurIPS), 2023.
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Citation
Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as …
Dingling Yao
,
Danru Xu
,
Perouz Taslakian
,
Sébastien Lachapelle
,
Sara Magliacane
,
Julius von Kügelgen
,
Francesco Locatello
Workshop at the Neural Information Processing Systems (NeurIPS), 2023.
PDF
Citation
Using Confounded Data in Latent Model-Based Reinforcement Learning
In the presence of confounding, naively using off-the-shelf offline reinforcement learning (RL) algorithms leads to sub-optimal …
Maxime Gasse
,
Damien Grasset
,
Pierre-Yves Oudeyer
,
Guillaume Gaudron
Transactions on Machine Learning Research (TMLR), 2023.
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Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …
Thibaud Godon
,
Baptiste Bauvin
,
Pascal Germain
,
Jacques Corbeil
,
Alexandre Drouin
Workshop on Spurious Correlations, Invariance, and Stability (ICML), 2023.
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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 …
Chris Chinenye Emezue
,
Alexandre Drouin
,
Tristan Deleu
,
Stefan Bauer
,
Yoshua Bengio
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML), 2023.
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Citation
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, …
Stephanie Long
,
Alexandre Piche
,
Valentina Zantedeschi
,
Tibor Schuster
,
Alexandre Drouin
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML), 2023.
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