About
People
Publications
Open Source
Demos
Events
Blog
Careers
Contact
English
English
Français
ServiceNow
ServiceNow Research
Tags
Causality
ServiceNow Research
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.
PDF
Cite
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.
PDF
Cite
Code
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.
PDF
Cite
Code
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.
Cite
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.
PDF
Cite
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
Cite
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.
PDF
Cite
Code
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.
PDF
Cite
Code
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.
PDF
Cite
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.
PDF
Cite
Code
Slides
Video
»
Cite
×