{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T23:53:30Z","timestamp":1775346810960,"version":"3.50.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U19A2065, 61976102, 62372210"],"award-info":[{"award-number":["U19A2065, 61976102, 62372210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            Estimating\n            <jats:italic>individual treatment effect<\/jats:italic>\n            (ITE) from observational data has attracted great interest in recent years, which plays a crucial role in decision-making across many high-impact domains such as economics, medicine, and e-commerce. Most existing studies of ITE estimation assume that different units at play are independent and do not influence each other. However, many social science experiments have shown that there often exist different levels of interactions between units in observational data, especially in a networked environment. As a result, the treatment assignment of one unit can affect the outcome of other units connected to it in the network, which is referred to as the\n            <jats:italic>interference<\/jats:italic>\n            or\n            <jats:italic>spillover effect<\/jats:italic>\n            . In this article, we study an important problem of ITE estimation from networked observational data by modeling the interference between different units and provide a principled framework to support such study. Methodologically, we propose a novel framework,\n            <jats:italic>SPNet<\/jats:italic>\n            , that first captures the influence of hidden confounders with the aid of graph convolutional network and then models the interference by introducing an environment summary variable and developing a masked attention mechanism. Experimental evaluations on several semi-synthetic datasets based on real-world networks corroborate the superiority of our proposed framework over state-of-the-art individual treatment effect estimation methods.\n          <\/jats:p>","DOI":"10.1145\/3628449","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T21:38:04Z","timestamp":1697665084000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0046-0923","authenticated-orcid":false,"given":"Qiang","family":"Huang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, International Center of Future Science, Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4237-6607","authenticated-orcid":false,"given":"Jing","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer and Data Sciences, Case Western Reserve University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-817X","authenticated-orcid":false,"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8522-6142","authenticated-orcid":false,"given":"Ruocheng","family":"Guo","sequence":"additional","affiliation":[{"name":"ByteDance Research, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4664-7147","authenticated-orcid":false,"given":"Huiyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, International Center of Future Science, Jilin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2697-8093","authenticated-orcid":false,"given":"Yi","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, International Center of Future Science, Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOAS1005"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11841"},{"key":"e_1_3_1_4_2","first-page":"3252","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Awan Usaid","year":"2020","unstructured":"Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. 2020. Almost-matching-exactly for treatment effect estimation under network interference. In International Conference on Artificial Intelligence and Statistics. PMLR, 3252\u20133262."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1323641"},{"issue":"5","key":"e_1_3_1_6_2","first-page":"861","article-title":"Parents and procedures: A randomized controlled trial","volume":"98","author":"Bauchner Howard","year":"1996","unstructured":"Howard Bauchner, Robert Vinci, Sharon Bak, Colleen Pearson, and Michael J. Corwin. 1996. Parents and procedures: A randomized controlled trial. Pediatrics 98, 5 (1996), 861\u2013867.","journal-title":"Pediatrics"},{"key":"e_1_3_1_7_2","unstructured":"David M. Blei Andrew Y. Ng and Michael I. Jordan. 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3 Jan (2003) 993\u20131022."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783296"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467302"},{"key":"e_1_3_1_11_2","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29 (2016) 3837\u20133845."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.d5888"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v14i1.7289"},{"key":"e_1_3_1_14_2","volume-title":"Proceedings of the KDD Workshop on Mining and Learning with Graphs (MLG\u201920)","volume":"8","author":"Fatemi Zahra","year":"2020","unstructured":"Zahra Fatemi and Elena Zheleva. 2020. Network experiment design for estimating direct treatment effects. In Proceedings of the KDD Workshop on Mining and Learning with Graphs (MLG\u201920), Vol. 8."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsa1906848"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1768100"},{"issue":"4","key":"e_1_3_1_17_2","first-page":"1","article-title":"A survey of learning causality with data: Problems and methods","volume":"53","author":"Guo Ruocheng","year":"2020","unstructured":"Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, and Huan Liu. 2020. A survey of learning causality with data: Problems and methods. ACM Comput. Surv. 53, 4 (2020), 1\u201337.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_1_18_2","first-page":"4534","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Guo Ruocheng","year":"2021","unstructured":"Ruocheng Guo, Jundong Li, Yichuan Li, K. Sel\u00e7uk Candan, Adrienne Raglin, and Huan Liu. 2021. Ignite: A minimax game toward learning individual treatment effects from networked observational data. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 4534\u20134540."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.31"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371816"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119030638"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5839"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000292"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751"},{"key":"e_1_3_1_26_2","unstructured":"Fredrik Johansson Uri Shalit and David Sontag. 2016. Learning representations for counterfactual inference. In International Conference on Machine Learning PMLR 3020\u20133029."},{"key":"e_1_3_1_27_2","unstructured":"Nathan Kallus Xiaojie Mao and Angela Zhou. 2019. Interval estimation of individual-level causal effects under unobserved confounding. In The 22nd International Conference on Artificial Intelligence and Statistics PMLR 2281\u20132290."},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Dongyeop Kang Waleed Ammar Bhavana Dalvi Madeleine van Zuylen Sebastian Kohlmeier Eduard Hovy and Roy Schwartz. 2018. A Dataset of Peer Reviews (PeerRead): Collection Insights and NLP Applications. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume 1 (Long Papers) Association for Computational Linguistics New Orleans Louisiana 1647\u20131661.","DOI":"10.18653\/v1\/N18-1149"},{"key":"e_1_3_1_29_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR\u201915)","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR\u201915), Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_1_30_2","volume-title":"Prceedings of the 2nd International Conference on Learning Representations (ICLR\u201914)","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes. In Prceedings of the 2nd International Conference on Learning Representations (ICLR\u201914), Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_1_31_2","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations (ICLR\u201917) Toulon France April 24-26 2017 Conference Track Proceedings."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2013.844698"},{"key":"e_1_3_1_33_2","unstructured":"Christos Louizos Uri Shalit Joris M. Mooij David Sontag Richard Zemel and Max Welling. 2017. Causal effect inference with deep latent-variable models. Advances in Neural Information Processing Systems 30 (2017) 6446\u20136456."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441818"},{"key":"e_1_3_1_35_2","first-page":"3700","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Ma Yunpu","year":"2021","unstructured":"Yunpu Ma and Volker Tresp. 2021. Causal inference under networked interference and intervention policy enhancement. In International Conference on Artificial Intelligence and Statistics. PMLR, 3700\u20133708."},{"key":"e_1_3_1_36_2","article-title":"Causal inference under networked interference","author":"Ma Yunpu","year":"2020","unstructured":"Yunpu Ma, Yuyi Wang, and Volker Tresp. 2020. Causal inference under networked interference. arXiv:2002.08506. Retrieved from https:\/\/arxiv.org\/abs\/2002.08506","journal-title":"arXiv:2002.08506"},{"key":"e_1_3_1_37_2","doi-asserted-by":"crossref","unstructured":"Elizabeth L. Ogburn Oleg Sofrygin Ivan Diaz and Mark J. Van der Laan. 2022. Causal inference for social network data. Journal of the American Statistical Association (2022) 1\u201315.","DOI":"10.1080\/01621459.2022.2131557"},{"key":"e_1_3_1_38_2","article-title":"Efficient treatment effect estimation in observational studies under heterogeneous partial interference","author":"Qu Zhaonan","year":"2021","unstructured":"Zhaonan Qu, Ruoxuan Xiong, Jizhou Liu, and Guido Imbens. 2021. Efficient treatment effect estimation in observational studies under heterogeneous partial interference. arXiv:2107.12420. Retrieved from https:\/\/arxiv.org\/abs\/2107.12420","journal-title":"arXiv:2107.12420"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269267"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_3_1_42_2","unstructured":"Usman Shahid and Elena Zheleva. 2019. Counterfactual learning in networks: an empirical study of model dependence. In Beyond Curve Fitting: Causation Counterfactuals and Imagination-based AI. AAAI Spring Symposium (AAAI-WHY 2019) Standford CA. Association for the Advancement of Artificial Intelligence."},{"key":"e_1_3_1_43_2","first-page":"3076","volume-title":"International Conference on Machine Learning","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning. 3076\u20133085."},{"key":"e_1_3_1_44_2","doi-asserted-by":"crossref","unstructured":"Cosma Rohilla Shalizi and Edward McFowland III. 2023. Estimating causal peer influence in homophilous social networks by inferring latent locations. Journal of the American Statistical Association 118 541 (2023) 707\u2013718.","DOI":"10.1080\/01621459.2021.1953506"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1177\/0049124111404820"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2956185"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0210-x"},{"key":"e_1_3_1_49_2","article-title":"Propensity score methodology in the presence of network entanglement between treatments","author":"Toulis Panos","year":"2018","unstructured":"Panos Toulis, Alexander Volfovsky, and Edoardo M. Airoldi. 2018. Propensity score methodology in the presence of network entanglement between treatments. arXiv:1801.07310. Retrieved from https:\/\/arxiv.org\/abs\/1801.07310","journal-title":"arXiv:1801.07310"},{"key":"e_1_3_1_50_2","article-title":"Using embeddings to correct for unobserved confounding in networks","volume":"32","author":"Veitch Victor","year":"2019","unstructured":"Victor Veitch, Yixin Wang, and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. Adv. Neural Inf. Process. Syst. 32 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.5555\/3546258.3546289"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.25.1.659"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao and Aidong Zhang. 2021. A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD) 15 5 (2021) 1\u201346.","DOI":"10.1145\/3444944"},{"key":"e_1_3_1_55_2","article-title":"Representation learning for treatment effect estimation from observational data","volume":"31","author":"Yao Liuyi","year":"2018","unstructured":"Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. 2018. Representation learning for treatment effect estimation from observational data. Adv. Neural Inf. Process. Syst. 31 (2018).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-004-0552-0"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3628449","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3628449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:52Z","timestamp":1750178212000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3628449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,9]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4,30]]}},"alternative-id":["10.1145\/3628449"],"URL":"https:\/\/doi.org\/10.1145\/3628449","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,9]]},"assertion":[{"value":"2022-09-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}