Learning Causal Effects on Hypergraphs (Extended Abstract) Article Swipe
Jing Ma
,
Mengting Wan
,
Longqi Yang
,
Jundong Li
,
Brent Hecht
,
Jaime Teevan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/721
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/721
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome of one individual should not be influenced by the treatment of other individuals (i.e., no interference), or assume the interference only exists between connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ITE estimations due to group interactions. We investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.
Related Topics
Concepts
Leverage (statistics)
Computer science
Causality (physics)
Hypergraph
Pairwise comparison
Theoretical computer science
Perspective (graphical)
Abstraction
Graph
Outcome (game theory)
Artificial intelligence
Machine learning
Mathematics
Discrete mathematics
Philosophy
Mathematical economics
Quantum mechanics
Physics
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2023/721
- https://www.ijcai.org/proceedings/2023/0721.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385763773
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385763773Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.24963/ijcai.2023/721Digital Object Identifier
- Title
-
Learning Causal Effects on Hypergraphs (Extended Abstract)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime TeevanList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2023/721Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2023/0721.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.ijcai.org/proceedings/2023/0721.pdfDirect OA link when available
- Concepts
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Leverage (statistics), Computer science, Causality (physics), Hypergraph, Pairwise comparison, Theoretical computer science, Perspective (graphical), Abstraction, Graph, Outcome (game theory), Artificial intelligence, Machine learning, Mathematics, Discrete mathematics, Philosophy, Mathematical economics, Quantum mechanics, Physics, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1995906082, https://openalex.org/W2012797835, https://openalex.org/W3106229813, https://openalex.org/W2962695761, https://openalex.org/W3123927413, https://openalex.org/W3037223051, https://openalex.org/W2156925274, https://openalex.org/W2892880750, https://openalex.org/W4290943920, https://openalex.org/W2996226706, https://openalex.org/W2945365184, https://openalex.org/W2314734263, https://openalex.org/W2613927341, https://openalex.org/W4295097398, https://openalex.org/W3154480910, https://openalex.org/W2112508839, https://openalex.org/W3106294663, https://openalex.org/W3159767375, https://openalex.org/W2996910665, https://openalex.org/W3085990079, https://openalex.org/W2951409510 |
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