Matthew James Vowels — accepted AI-conference papers

2026

CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions

Matthew James Vowels, Mathieu Rochat, Sina Akbari

ICLR 2026poster

Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability to inherently respect causal structures can limit t…

Cited by 0 Source ↗code ↗Scholar ↗

2023

Causal Effect Identification in Uncertain Causal Networks

Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew James Vowels, Jalal Etesami, Negar Kiyavash

NeurIPS 2023poster

Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correctly specified causal structure. In this work…

Cited by 5 Source ↗PDF ↗Scholar ↗