Learning Individual Treatment Effects under Heterogeneous Interference in Networks
Authors: 
Ziyu Zhao, Yuqi Bai, Ruoxuan Xiong, Qingyu Cao, Chao Ma, Ning Jiang, Fei Wu, Kun KuangAuthors Info & Claims
Ziyu Zhao, Yuqi Bai, Ruoxuan Xiong, Qingyu Cao, Chao Ma, Ning Jiang, Fei Wu, Kun KuangAuthors Info & ClaimsArticle No.: 199, Pages 1 - 21
Abstract
Estimating individual treatment effects in networked observational data is a crucial and increasingly recognized problem. One major challenge of this problem is violating the stable unit treatment value assumption (SUTVA), which posits that a unit’s outcome is independent of others’ treatment assignments. However, in network data, a unit’s outcome is influenced not only by its treatment (i.e., direct effect) but also by the treatments of others (i.e., spillover effect) since the presence of interference. Moreover, the interference from other units is always heterogeneous (e.g., friends with similar interests have a different influence than those with different interests). In this article, we focus on the problem of estimating individual treatment effects (including direct effect and spillover effect) under heterogeneous interference in networks. To address this problem, we propose a novel dual weighting regression (DWR) algorithm by simultaneously learning attention weights to capture the heterogeneous interference from neighbors and sample weights to eliminate the complex confounding bias in networks. We formulate the learning process as a bi-level optimization problem. Theoretically, we give a generalization error bound for the expected estimation error of the individual treatment effects. Extensive experiments on four benchmark datasets demonstrate that the proposed DWR algorithm outperforms the state-of-the-art methods in estimating individual treatment effects under heterogeneous network interference.
References
[1]
Peter M. Aronow and Cyrus Samii. 2017. Estimating average causal effects under general interference, with application to a social network experiment. The Annals of Applied Statistics 11, 4 (2017), 1912–1947.
[2]
Alexis Bellot, Anish Dhir, and Giulia Prando. 2022. Generalization bounds and algorithms for estimating conditional average treatment effect of dosage. arXiv:2205.14692. Retrieved from https://arxiv.org/abs/2205.14692
[3]
Rohit Bhattacharya, Daniel Malinsky, and Ilya Shpitser. 2020. Causal inference under interference and network uncertainty. In Proceedings of the 35th Uncertainty in Artificial Intelligence Conference. R. P. Adams and V. Gogate (Eds.), PMLR, 1028–1038.
[4]
Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, and Ronghua Luo. 2023. Heterogeneous graph contrastive learning for recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 544–552.
[5]
Zhixuan Chu, Stephen L. Rathbun, and Sheng Li. 2021. Graph infomax adversarial learning for treatment effect estimation with networked observational data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, 176–184. DOI:
[6]
David Roxbee Cox. 1958. Planning of Experiments. Wiley.
[7]
Irina Cristali and Victor Veitch. 2022. Using embeddings for causal estimation of peer influence in social networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35. 15616–15628.
[8]
Laura Forastiere, Edoardo M. Airoldi, and Fabrizia Mealli. 2021. Identification and estimation of treatment and interference effects in observational studies on networks. Journal of the American Statistical Association 116, 534 (2021), 901–918.
[9]
Andrew Giffin, B. J. Reich, Shu Yang, and A. G. Rappold. 2023. Generalized propensity score approach to causal inference with spatial interference. Biometrics 79, 3 (2023), 2220–2231.
[10]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855–864.
[11]
Ruocheng Guo, Jundong Li, and Huan Liu. 2020. Learning individual causal effects from networked observational data. In Proceedings of the 13th International Conference on Web Search and Data Mining. 232–240.
[12]
Jens Hainmueller. 2012. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis 20, 1 (2012), 25–46.
[13]
Song Jiang and Yizhou Sun. 2022. Estimating causal effects on networked observational data via representation learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 852–861.
[14]
Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen, and Hanghang Tong. 2022. X-GOAL: Multiplex heterogeneous graph prototypical contrastive learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 894–904.
[15]
Fredrik Johansson, Uri Shalit, and David Sontag. 2016. Learning representations for counterfactual inference. In Proceedings of the International Conference on Machine Learning. PMLR, 3020–3029.
[16]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https://arxiv.org/abs/1609.02907
[17]
Kun Kuang, Peng Cui, Bo Li, Meng Jiang, and Shiqiang Yang. 2017. Estimating treatment effect in the wild via differentiated confounder balancing. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 265–274.
[18]
John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, and Eunyee Koh. 2019. Attention models in graphs: A survey. ACM Transactions on Knowledge Discovery from Data (TKDD) 13, 6 (2019), 1–25.
[19]
Jure Leskovec and Rok Sosič. 2016. SNAP: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST) 8, 1 (2016), 1.
[20]
Shuangning Li and Stefan Wager. 2022. Random graph asymptotics for treatment effect estimation under network interference. The Annals of Statistics. 50, 4 (2022), 2334–2358.
[21]
Yunzhe Li, Kun Kuang, Bo Li, Peng Cui, Jianrong Tao, Hongxia Yang, and Fei Wu. 2020. Continuous treatment effect estimation via generative adversarial de-confounding. In Proceedings of the 2020 KDD Workshop on Causal Discovery. PMLR, 4–22.
[22]
Lan Liu, Michael G. Hudgens, and Sylvia Becker-Dreps. 2016. On inverse probability-weighted estimators in the presence of interference. Biometrika 103, 4 (2016), 829–842.
[23]
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, and Max Welling. 2017. Causal effect inference with deep latent-variable models. Advances in Neural Information Processing Systems. 30.
[24]
Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 287–296.
[25]
Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, and Jaime Teevan. 2022. Learning causal effects on hypergraphs. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1202–1212.
[26]
Yunpu Ma and Volker Tresp. 2021. Causal inference under networked interference and intervention policy enhancement. In Proceedings of the International Conference on Artificial Intelligence and Statistics. PMLR, 3700–3708.
[27]
Kristin L. Nichol, April Lind, Karen L. Margolis, Maureen Murdoch, Rodney McFadden, Meri Hauge, Sanne Magnan, and Mari Drake. 1995. The effectiveness of vaccination against influenza in healthy, working adults. New England Journal of Medicine 333, 14 (1995), 889–893.
[28]
Elizabeth L. Ogburn, Oleg Sofrygin, Ivan Diaz, and Mark J. Van Der Laan. 2024. Causal inference for social network data. Journal of the American Statistical Association. 119, 545 (2024), 597–611.
[29]
Yunhe Pan. 2023. structure analysis of crowd intelligence systems. Engineering 25 (2023), 17–20.
[30]
Petr Parshakov, Iuliia Naidenova, and Angel Barajas. 2020. Spillover effect in promotion: Evidence from video game publishers and eSports tournaments. Journal of Business Research 118 (2020), 262–270.
[31]
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
[32]
Ryan A. Rossi and Nesreen K. Ahmed. 2016. An interactive data repository with visual analytics. ACM SIGKDD Explorations Newsletter 17, 2 (2016), 37–41.
[33]
Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the International Conference on Machine Learning. PMLR, 3076–3085.
[34]
Michael E. Sobel. 2006. What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. Journal of the American Statistical Association 101, 476 (2006), 1398–1407.
[35]
Oleg Sofrygin and Mark J. van der Laan. 2017. Semi-parametric estimation and inference for the mean outcome of the single time-point intervention in a causally connected population. Journal of Causal Inference 5, 1 (2017), 20160003.
[36]
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 555–563.
[37]
Eric J. Tchetgen Tchetgen, Isabel R. Fulcher, and Ilya Shpitser. 2021. Auto-G-computation of causal effects on a network. Journal of the American Statistical Association 116, 534 (2021), 833–844.
[38]
Mark J. Van der Laan. 2014. Causal inference for a population of causally connected units. Journal of Causal Inference 2, 1 (2014), 13–74.
[39]
Victor Veitch, Yixin Wang, and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32. 13792–13802.
[40]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903. Retrieved from https://arxiv.org/abs/1710.10903
[41]
Stefan Wager and Susan Athey. 2018. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association 113, 523 (2018), 1228–1242.
[42]
Justin R. Williams and Catherine M. Crespi. 2020. Causal inference for multiple continuous exposures via the multivariate generalized propensity score. arXiv:2008.13767. Retrieved from https://arxiv.org/abs/2008.13767
[43]
Jimin Xu, Nuanxin Hong, Zhening Xu, Zhou Zhao, Chao Wu, Kun Kuang, Jiaping Wang, Mingjie Zhu, Jingren Zhou, Kui Ren, Xiaohu Yang, Cewu Lu, Jian Pei, and Harry Shum. 2023. Data-driven learning for data rights, data pricing, and privacy computing. Engineering 25 (2023), 66–76.
[44]
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. 2018. GANITE: Estimation of individualized treatment effects using generative adversarial nets. In Proceedings of the International Conference on Learning Representations.
[45]
Zhao Ziyu, Kun Kuang, Bo Li, Peng Cui, Runze Wu, Jun Xiao, and Fei Wu. 2023. Differentiated matching for individual and average treatment effect estimation. Data Mining and Knowledge Discovery 37, 1 (2023), 205–227.
[46]
Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, and Yue He. 2020. Counterfactual prediction for bundle treatment. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33. 19705–19715.
[47]
Hao Zou, Kun Kuang, Boqi Chen, Peixuan Chen, and Peng Cui. 2019. Focused context balancing for robust offline policy evaluation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 696–704.
Index Terms
- Learning Individual Treatment Effects under Heterogeneous Interference in Networks
Recommendations
Learning triggers for heterogeneous treatment effects
AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial IntelligenceThe causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has ...
Estimating Treatment Effects Under Heterogeneous Interference
Machine Learning and Knowledge Discovery in Databases: Research TrackAbstractTreatment effect estimation can assist in effective decision-making in e-commerce, medicine, and education. One popular application of this estimation lies in the prediction of the impact of a treatment (e.g., a promotion) on an outcome (e.g., ...
Comments
Information & Contributors
Information
Published In
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 August 2024
Online AM: 18 June 2024
Accepted: 02 June 2024
Revised: 10 April 2024
Received: 30 November 2022
Published in TKDD Volume 18, Issue 8
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Starry Night Science Fund of Zhejiang University Shanghai Institute
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 222Total Downloads
- Downloads (Last 12 months)222
- Downloads (Last 6 weeks)40
Reflects downloads up to 24 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in