{"type":"ScholarlyArticle","title":"Learning Causal Effects on Hypergraphs (Extended Abstract)","authors":[{"name":"Jing Ma","slug":"jing-ma","given":"Jing","family":"Ma","affiliation":"University of Virginia , Charlottesville , VA , USA"},{"name":"Mengting Wan","slug":"mengting-wan","given":"Mengting","family":"Wan","affiliation":"Microsoft , Redmond , WA , USA"},{"name":"Longqi Yang","slug":"longqi-yang","given":"Longqi","family":"Yang","affiliation":"Microsoft , Redmond , WA , USA"},{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":"University of Virginia , Charlottesville , VA , USA"},{"name":"Brent Hecht","slug":"brent-hecht","given":"Brent","family":"Hecht","affiliation":"Microsoft , Redmond , WA , USA"},{"name":"Jaime Teevan","slug":"jaime-teevan","given":"Jaime","family":"Teevan","affiliation":"Microsoft , Redmond , WA , USA"}],"abstract":"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.","doi":"10.24963/ijcai.2023/721","url":"https://ijcai.org/proceedings/2023/721","pdf":"https://ijcai.org/proceedings/2023/0721.pdf","markdown":"https://ijcai.org/proceedings/2023/721.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/721","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":721,"pages":{"first":6463,"last":6467},"keywords":["Sister Conferences Best Papers: Machine Learning","Sister Conferences Best Papers: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Ahluwalia et al., 2001] Rohini Ahluwalia, H Rao Unnava, and Robert E Burnkrant. The moderating role of commit- ment on the spillover effect of marketing communications. Journal of Marketing research, 38(4):458-470, 2001.","title":"The moderating role of commitment on the spillover effect of marketing communications","authors":"Rohini Ahluwalia, H Rao Unnava, Robert E Burnkrant","year":"2001","venue":"Journal of Marketing research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Aronow and Samii, 2017] Peter M Aronow and Cyrus Samii. Estimating average causal effects under general interference, with application to a social network experi- ment. The Annals of Applied Statistics, 11(4):1912-1947, 2017.","title":"Estimating average causal effects under general interference, with application to a social network experiment","authors":"Samii ; Aronow, M Peter, Cyrus Aronow, Samii","year":"2017","venue":"The Annals of Applied Statistics","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bai et al., 2021] Song Bai, Feihu Zhang, and Philip HS Torr. Hypergraph convolution and hypergraph attention. Pattern Recognition, 110:107637, 2021.","title":"Hypergraph convolution and hypergraph attention","authors":"Song Bai, Feihu Zhang, Philip Hs Torr","year":"2021","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Basse and Feller, 2018] Guillaume Basse and Avi Feller. Analyzing two-stage experiments in the presence of inter- ference. Journal of the American Statistical Association, 113(521):41-55, 2018.","title":"Analyzing two-stage experiments in the presence of interference","authors":"Feller Basse, Guillaume Basse, Avi Feller","year":"2018","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ding et al., 2020] Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, and Huan Liu. Be more with less: Hyper- graph attention networks for inductive text classification. arXiv preprint arXiv:2011.00387, 2020.","title":"Be more with less: Hypergraph attention networks for inductive text classification","authors":"Ding","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Feng et al., 2019] Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. Hypergraph neural networks. In Proceedings of the AAAI Conference on Ar- tificial Intelligence, volume 33, pages 3558-3565, 2019.","title":"Hypergraph neural networks","authors":"Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fisher, 1936] Ronald Aylmer Fisher. Design of experi- ments. Br Med J, 1(3923):554-554, 1936.","title":"Design of experiments","authors":"Aylmer Ronald, Fisher","year":"1936","venue":"Br Med J","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guo et al., 2020] Ruocheng Guo, Jundong Li, and Huan Liu. Learning individual causal effects from networked observational data. In Proceedings of the 13th Interna- tional Conference on Web Search and Data Mining, pages 232-240, 2020.","title":"Learning individual causal effects from networked observational data","authors":"Guo","year":"2020","venue":"Proceedings of the 13th International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hudgens and Halloran, 2008] Michael G Hudgens and M Elizabeth Halloran. Toward causal inference with inter- ference. Journal of the American Statistical Association, 103(482):832-842, 2008.","title":"Toward causal inference with interference","authors":"G Michael, M Hudgens, Elizabeth Halloran","year":"2008","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Imai et al., 2020] Kosuke Imai, Zhichao Jiang, and Anup Malani. Causal inference with interference and noncom- pliance in two-stage randomized experiments. Journal of the American Statistical Association, pages 1-13, 2020.","title":"Causal inference with interference and noncompliance in two-stage randomized experiments","authors":"Kosuke Imai, Zhichao Jiang, Anup Malani","year":"2020","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kohavi et al., 2013] Ron Kohavi, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, and Nils Pohlmann. Online con- trolled experiments at large scale. In Proceedings of the 19th ACM SIGKDD international conference on Knowl- edge discovery and data mining, pages 1168-1176, 2013.","title":"Online controlled experiments at large scale","authors":"Ron Kohavi, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, Nils Pohlmann","year":"2013","venue":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Louizos et al., 2017] Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling. Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems, 2017.","title":"Causal effect inference with deep latent-variable models","authors":"Christos Louizos, Uri Shalit, M Joris, David Mooij, Richard Sontag, Max Zemel, Welling","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ma and Tresp, 2021] Yunpu Ma and Volker Tresp. Causal inference under networked interference and intervention policy enhancement. In International Conference on Arti- ficial Intelligence and Statistics, pages 3700-3708. PMLR, 2021.","title":"Yunpu Ma and Volker Tresp. Causal inference under networked interference and intervention policy enhancement","authors":"Tresp Ma","year":"2021","venue":"International Conference on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ma et al., 2022] Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, and Jaime Teevan. Learning causal effects on hypergraphs. In ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, 2022.","title":"Learning causal effects on hypergraphs","authors":"Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime Teevan","year":"2022","venue":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mitchell, 1974] J Clyde Mitchell. Social networks. Annual review of anthropology, 3(1):279-299, 1974.","title":"Social networks","authors":"Clyde Mitchell","year":"1974","venue":"Annual review of anthropology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rubin, 1980] Donald B Rubin. Randomization analysis of experimental data: The fisher randomization test com- ment. Journal of the American Statistical Association, 75(371):591-593, 1980.","title":"Randomization analysis of experimental data: The fisher randomization test comment","authors":"Donald B Rubin","year":"1980","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shalit et al., 2017] Uri Shalit, Fredrik D Johansson, and David Sontag. Estimating individual treatment effect: gen- eralization bounds and algorithms. In International Con- ference on Machine Learning, 2017.","title":"Estimating individual treatment effect: generalization bounds and algorithms","authors":"Shalit","year":"2017","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Splawa-Neyman et al., 1990] Jerzy Splawa-Neyman, Dorota M Dabrowska, and TP Speed. On the application of probability theory to agricultural experiments. essay on principles. section 9. Statistical Science, pages 465-472, 1990.","title":"On the application of probability theory to agricultural experiments","authors":"Jerzy Splawa-Neyman, Dorota M Dabrowska, Speed","year":"1990","venue":"essay on principles. section 9","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tchetgen and VanderWeele, 2012] Eric J Tchetgen Tchet- gen and Tyler J VanderWeele. On causal inference in the presence of interference. Statistical methods in medical research, 21(1):55-75, 2012.","title":"On causal inference in the presence of interference","authors":"Eric J, Tchetgen Tchetgen, Tyler J Vanderweele","year":"2012","venue":"Statistical methods in medical research","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ugander et al., 2013] Johan Ugander, Brian Karrer, Lars Backstrom, and Jon Kleinberg. Graph cluster randomiza- tion: Network exposure to multiple universes. In Proceed- ings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 329-337, 2013.","title":"Graph cluster randomization: Network exposure to multiple universes","authors":"Johan Ugander, Brian Karrer, Lars Backstrom, Jon Kleinberg","year":"2013","venue":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yadati et al., 2019] Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. Hypergcn: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems, 32, 2019.","title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs","authors":"Yadati","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yilmaz et al., 2002] Serdar Yilmaz, Kingley E Haynes, and Mustafa Dinc. Geographic and network neighbors: Spillover effects of telecommunications infrastructure. Journal of Regional Science, 42(2):339-360, 2002.","title":"Geographic and network neighbors: Spillover effects of telecommunications infrastructure","authors":"Serdar Yilmaz, Kingley E Haynes, Mustafa Dinc","year":"2002","venue":"Journal of Regional Science","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yuan et al., 2021] Yuan Yuan, Kristen Altenburger, and Far- shad Kooti. Causal network motifs: Identifying heteroge- neous spillover effects in a/b tests. In Proceedings of the Web Conference 2021, pages 3359-3370, 2021.","title":"Causal network motifs: Identifying heterogeneous spillover effects in a/b tests","authors":"Yuan Yuan, Kristen Altenburger, Farshad Kooti","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang et al., 2019] Ruochi Zhang, Yuesong Zou, and Jian Ma. Hyper-sagnn: a self-attention based graph neural net- work for hypergraphs. arXiv preprint arXiv:1911.02613, 2019.","title":"Hyper-sagnn: a self-attention based graph neural network for hypergraphs","authors":"Zhang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}