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Kristin Bennett

3 accepted papers

2023

Pairwise Causality Guided Transformers for Event Sequences

NeurIPS 2023poster

Although pairwise causal relations have been extensively studied in observational longitudinal analyses across many disciplines, incorporating knowledge of causal pairs into deep learning models for temporal event sequences remains largely unexplored. In this paper, we propose a novel approach for e…

Cited by 3SourcePDFScholar
2023

Probabilistic Attention-to-Influence Neural Models for Event Sequences

ICML 2023poster

Discovering knowledge about which types of events influence others, using datasets of event sequences without time stamps, has several practical applications. While neural sequence models are able to capture complex and potentially long-range historical dependencies, they often lack the interpretabi…

Cited by 3SourcePDFScholar
2021

Causal Inference for Event Pairs in Multivariate Point Processes

NeurIPS 2021poster

Causal inference and discovery from observational data has been extensively studied across multiple fields. However, most prior work has focused on independent and identically distributed (i.i.d.) data. In this paper, we propose a formalization for causal inference between pairs of event variables i…

Cited by 15SourcePDFScholar