mlsanityData to 14 Oct 2026

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

arXiv 2002.07906 ↗First posted 18 Feb 2020cs.LG stat.MLcited by 36

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Abstract (arXiv)

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.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2020.

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Citing papers per year: 2021: 6; 2022: 2; 2023: 8; 2024: 4; 2025: 10; 2026: 6.

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  1. 2021Neural Temporal Point Processes: A Reviewcited by 114
  2. 2021Transformer Embeddings of Irregularly Spaced Events and Their Participantscited by 93
  3. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  4. 2022Exploring Generative Neural Temporal Point Processcited by 30
  5. 2023Add and Thin: Diffusion for Temporal Point Processescited by 23
  6. 2021Counterfactual Temporal Point Processescited by 20
  7. 2021THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequencescited by 19
  8. 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
  9. 2024Unlocking Point Processes through Point Set Diffusioncited by 14
  10. 2021An Empirical Study: Extensive Deep Temporal Point Processcited by 10
  11. 2025Edit-Based Flow Matching for Temporal Point Processescited by 8
  12. 2024Learning Granger Causality from Instance-wise Self-attentive Hawkes Processescited by 7
24 more
  1. 2022Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processescited by 6
  2. 2025MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoningcited by 5
  3. 2025Neural Spatiotemporal Point Processes: Trends and Challengescited by 5
  4. 2023TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequencescited by 3
  5. 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 3
  6. 2024Interpretable Neural Temporal Point Processes for Modelling Electronic Health Recordscited by 2
  7. 2025Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Modelcited by 2
  8. 2026Your Autoregressive Model Already Reveals the Causal Graphcited by 2
  9. 2023Causal Graph Discovery from Self and Mutually Exciting Time Seriescited by 1
  10. 2025Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Datacited by 1
  11. 2021Causal Graph Discovery from Self and Mutually Exciting Time Seriescited by 0
  12. 2023Reinforcement Logic Rule Learning for Temporal Point Processescited by 0
  13. 2023Hawkes Processes with Delayed Granger Causalitycited by 0
  14. 2023Explainable History Distillation by Marked Temporal Point Processcited by 0
  15. 2024Transformers with Sparse Attention for Granger Causalitycited by 0
  16. 2025Uncovering Causal Relation Shifts in Event Sequences under Out-of-Domain Interventionscited by 0
  17. 2025TempOpt -- Unsupervised Alarm Relation Learning for Telecommunication Networkscited by 0
  18. 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networkscited by 0
  19. 2025MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processescited by 0
  20. 2026Function-Valued Causal Influence in Nonlinear Time Seriescited by 0
  21. 2026Resilient Concurrent Causal Discovery for Topological Event Sequencescited by 0
  22. 2026Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budgetcited by 0
  23. 2026seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequencescited by 0
  24. 2026Inferring Causal Relations between Two Sequences of Events with Language Modelscited by 0