Causal Diagrams for Interference
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Abstract (arXiv)
The term "interference" has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual's treatment on another individual's outcome; we call this direct interference. Interference by contagion is present when one individual's outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual's outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals may affect one another's outcomes in any number of ways. In many settings, more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2014.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 6; 2025: 3; 2026: 2.
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- 2023Generalization bound for estimating causal effects from observational network datacited by 11
- 2024Do Finetti: On Causal Effects for Exchangeable Datacited by 11
- 2026Generative AI collective behavior needs an interactionist paradigmcited by 6
- 2021Disaggregated Interventions to Reduce Inequalitycited by 5
- 2022Heterogeneous Peer Effects in the Linear Threshold Modelcited by 5
- 2023Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influencecited by 5
- 2022Relational Causal Models with Cycles:Representation and Reasoningcited by 4
- 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effectscited by 4
- 2024Optimizing Treatment Allocation in the Presence of Interferencecited by 4
- 2023SpaCE: The Spatial Confounding Environmentcited by 3
- 2024Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interferencecited by 2
- 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
6 more
- 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Modelscited by 1
- 2024Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experimentscited by 0
- 2024Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confoundingcited by 0
- 2025Causally Fair Node Classification on Non-IID Graph Datacited by 0
- 2025Estimating Treatment Effects in Networks under Unknown Exposure Mappingscited by 0
- 2026Causal Inference under Interference with Learned Exposure Mappingscited by 0
- 2026Causal Inference under Interference with Learned Exposure Mappings26 Jul 2026
- 2026Generative AI collective behavior needs an interactionist paradigm15 Jan 2026
- 2025Estimating Treatment Effects in Networks under Unknown Exposure Mappings24 Oct 2025
- 2025Causally Fair Node Classification on Non-IID Graph Data3 May 2025
- 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Models24 Mar 2025
- 2024Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding2 Nov 2024
- 2024Optimizing Treatment Allocation in the Presence of Interference30 Sep 2024
- 2024Do Finetti: On Causal Effects for Exchangeable Data29 May 2024
- 2024Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference30 Apr 2024
- 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects17 Mar 2024
- 2024Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments20 Feb 2024
- 2023SpaCE: The Spatial Confounding Environment1 Dec 2023
6 more
- 2023Generalization bound for estimating causal effects from observational network data8 Aug 2023
- 2023Contagion Effect Estimation Using Proximal Embeddings4 Jun 2023
- 2023Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence27 May 2023
- 2022Relational Causal Models with Cycles:Representation and Reasoning22 Feb 2022
- 2022Heterogeneous Peer Effects in the Linear Threshold Model27 Jan 2022
- 2021Disaggregated Interventions to Reduce Inequality1 Jul 2021
- 2023Generalization bound for estimating causal effects from observational network datacited by 11
- 2024Do Finetti: On Causal Effects for Exchangeable Datacited by 11
- 2026Generative AI collective behavior needs an interactionist paradigmcited by 6
- 2021Disaggregated Interventions to Reduce Inequalitycited by 5
- 2022Heterogeneous Peer Effects in the Linear Threshold Modelcited by 5
- 2023Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influencecited by 5
- 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effectscited by 4
- 2024Optimizing Treatment Allocation in the Presence of Interferencecited by 4
- 2023SpaCE: The Spatial Confounding Environmentcited by 3
- 2024Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interferencecited by 2
- 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
- 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Modelscited by 1
12 most cited of 17 All 17 (JSON)
- 2026Causal Inference under Interference with Learned Exposure Mappings26 Jul 2026
- 2026Generative AI collective behavior needs an interactionist paradigm15 Jan 2026
- 2025Estimating Treatment Effects in Networks under Unknown Exposure Mappings24 Oct 2025
- 2025Causally Fair Node Classification on Non-IID Graph Data3 May 2025
- 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Models24 Mar 2025
- 2024Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding2 Nov 2024
- 2024Optimizing Treatment Allocation in the Presence of Interference30 Sep 2024
- 2024Do Finetti: On Causal Effects for Exchangeable Data29 May 2024
- 2024Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference30 Apr 2024
- 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects17 Mar 2024
- 2024Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments20 Feb 2024
- 2023SpaCE: The Spatial Confounding Environment1 Dec 2023
12 newest of 17 All 17 (JSON)