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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Wei Zhang · Thomas Panum · Somesh Jha · Prasad Chalasani · David Page

Tue Jul 14 09:00 AM -- 09:45 AM & Tue Jul 14 08:00 PM -- 08:45 PM (PDT) @

We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency. To address these weaknesses, we propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic attribution method. Across multiple datasets riddled with diverse event interdependency, we demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.

Author Information

Wei Zhang (Facebook Inc.)
Thomas Panum (Aalborg University)
Somesh Jha (University of Wisconsin, Madison)
Prasad Chalasani (XaiPient)

Prasad Chalasani is CEO of XaiPient, whose mission is Explainable AI for Humans. He has a BTech in Computer. Science from IIT, Kharagpur, and a PhD in ML from Carnegie Mellon University. His previous roles include Quant Researcher and Portfolio Manager at hedge funds (WorldQuant, HBK), and he has lead quant research and data science teams at Goldman Sachs and Yahoo. Most recently he was Chief Scientist at MediaMath, leading ML for advertising.

David Page (Duke)

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