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Causal Effect Estimation on Hierarchical Spatial Graph Data

Authors:

Hisashi Kashima,

Masaki OnishiAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2145 - 2154

https://doi.org/10.1145/3580305.3599269

Published: 04 August 2023 Publication History

Abstract

Estimating individual treatment effects from observational data is a fundamental problem in causal inference. To accurately estimate treatment effects in the spatial domain, we need to address certain aspects such as how to use the spatial coordinates of covariates and treatments and how the covariates and the treatments interact spatially. We introduce a new problem of predicting treatment effects on time series outcomes from spatial graph data with a hierarchical structure. To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome. Using a multi-agent simulator, we synthesized a crowd movement guidance dataset and conduct experiments to estimate the conditional average treatment effect, where we considered the initial locations of the crowds as covariates, route guidance as a treatment, and number of agents reaching a goal at each time stamp as the outcome. We employed state-of-the-art spatio-temporal graph neural networks and neural network-based causal inference methods as baselines, and show that our proposed method outperformed baselines both quantitatively and qualitatively.

References

[1]

Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In Advances in Neural Information Processing Systems.

[2]

Ioana Bica, James Jordon, and Mihaela van der Schaar. 2020. Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. In Advances in Neural Information Processing Systems.

[3]

Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson. 2013. Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising. Journal of Machine Learning Research 14, 101 (2013), 3207--3260.

Digital Library

[4]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In Proceedings of the 2nd International Conference on Learning Representations.

[5]

Luc G Chalmet, Richard L Francis, and Patsy B Saunders. 1982. Network models for building evacuation. Management science 28, 1 (1982), 86--105.

[6]

Ravi Sekhar Chalumuri and Yasuo Asakura. 2014. Modelling travel time distribution under various uncertainties on Hanshin expressway of Japan. European Transport Research Review 6, 1 (2014), 85--92.

[7]

Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. 2021. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589, 7840 (2021), 82--87.

[8]

Theodoros Chondrogiannis, Panagiotis Bouros, and Winfried Emser. 2021. Simulation-Based Evacuation Planning for Urban Areas. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems.

Digital Library

[9]

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 and Data Mining.

Digital Library

[10]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems.

[11]

Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, and Kazuya Takeda. 2022. Estimating Counterfactual Treatment Outcomes over Time in Multi-Vehicle Simulation. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems.

Digital Library

[12]

Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. 2019. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence.

Digital Library

[13]

Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. 2021. Hierarchical Graph Convolution Network for Traffic Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence.

[14]

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.

Digital Library

[15]

Shonosuke Harada and Hisashi Kashima. 2021. GraphITE: Estimating Individual Effects of Graph-structured Treatments. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management.

Digital Library

[16]

Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J Kusner, and Ricardo Silva. 2021. Causal Effect Inference for Structured Treatments. In Advances in Neural Information Processing Systems.

[17]

Yapeng Li, Wei Cai, and Austin A. Kana. 2019. Design of level of service on facilities for crowd evacuation using genetic algorithm optimization. Safety Science 120 (2019), 237--247.

[18]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In The 6th International Conference on Learning Representations.

[19]

Xingchao Liu, Xing Han, Na Zhang, and Qiang Liu. 2020. Certified Monotonic Neural Networks. Advances in Neural Information Processing Systems (2020).

[20]

Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael Jordan, Yuan Qi, and Le Song. 2020. Cost-Effective Incentive Allocation via Structured Counterfactual Inference. In Proceedings of the AAAI Conference on Artificial Intelligence.

[21]

Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, and Jundong Li. 2022. Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US. In Proceedings of the ACM Web Conference 2022.

Digital Library

[22]

Jing Ma, Ruocheng Guo, Chen Chen, Aidong Zhang, and Jundong Li. 2021. De-confounding with Networked Observational Data in a Dynamic Environment. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining.

Digital Library

[23]

Jing Ma and Jundong Li. 2022. Learning Causality with Graphs. AI Magazine 43, 4 (2022), 365--375.

Digital Library

[24]

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.

Digital Library

[25]

Yunpu Ma and Volker Tresp. 2021. Causal Inference under Networked Interference and Intervention Policy Enhancement. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics.

[26]

Georgia Papadogeorgou, Kosuke Imai, Jason Lyall, and Fan Li. 2022. Causal infer-ence with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq. Journal of the Royal Statistical Society Series B 84, 5 (November 2022), 1969--1999.

[27]

Anatolii Prokhorchuk, Justin Dauwels, and Patrick Jaillet. 2020. Estimating Travel Time Distributions by Bayesian Network Inference. IEEE Transactions on Intelligent Transportation Systems 21, 5 (2020), 1867--1876.

[28]

Donald B. Rubin. 2005. Causal Inference Using Potential Outcomes. J. Amer. Statist. Assoc. 100, 469 (2005), 322--331.

[29]

Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of ICML.

[30]

Joseph Sill. 1998. Monotonic Networks. In Advances in Neural Information Pro-cessing Systems.

[31]

Koh Takeuchi, Ryo Nishida, Hisashi Kashima, and Masaki Onishi. 2021. Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems.

Digital Library

[32]

Wouter G Van Toll, Atlas F Cook IV, and Roland Geraerts. 2012. Real-time density-based crowd simulation. Computer Animation and Virtual Worlds 23, 1 (2012), 59--69.

Digital Library

[33]

Jinghong Wang, Bowei Jin, Jia Li, Fanghao Chen, Zhirong Wang, and Jinhua Sun. 2019. Method for guiding crowd evacuation at exit: The buffer zone. Safety Science 118 (2019), 88--95.

[34]

Yixin Wang and David M. Blei. 2019. The Blessings of Multiple Causes. J. Amer. Statist. Assoc. 114, 528 (2019), 1574--1596.

[35]

Antoine Wehenkel and Gilles Louppe. 2019. Unconstrained Monotonic Neural Networks. In Advances in Neural Information Processing Systems.

[36]

Yair Wiseman, Peng Chen, Rui Tong, Guangquan Lu, and Yunpeng Wang. 2018. Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing. Journal of Advanced Transportation 2018 (2018), 3747632.

[37]

Brian Wolshon, Alison Catarella-Michel, and Laurence Lambert. 2006. Louisiana Highway Evacuation Plan for Hurricane Katrina: Proactive Management of a Regional Evacuation. Journal of Transportation Engineering 132, 1 (2006), 1--10.

[38]

Sai-Keung Wong, Yu-Shuen Wang, Pao-Kun Tang, and Tsung-Yu Tsai. 2017. Optimized evacuation route based on crowd simulation. Computational Visual Media 3, 3 (2017), 243--261.

[39]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Digital Library

[40]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence.

Digital Library

[41]

Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, and Satish V. Ukkusuri. 2019. Predicting Evacuation Decisions Using Representations of Individuals' Pre-Disaster Web Search Behavior. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[42]

Tomohisa Yamashita, Takashi Okada, and Itsuki Noda. 2013. Implementation of Simulation Environment for Exhaustive Analysis of Huge-Scale Pedestrian Flow. SICE Journal of Control, Measurement, and System Integration 6, 2 (2013), 137--146.

[43]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In Proceedings of the AAAI Conference on Artificial Intelligence.

Digital Library

[44]

Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, and Baocai Yin. 2021. Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions. IEEE Transactions on Intelligent Transportation Systems 23, 6 (2021), 4927--4943.

[45]

Seungil You, David Ding, Kevin Canini, Jan Pfeifer, and Maya Gupta. 2017. Deep Lattice Networks and Partial Monotonic Functions. In Advances in Neural Information Processing Systems.

[46]

Zhengxu Yu, Shuxian Liang, Long Wei, Zhongming Jin, Jianqiang Huang, Deng Cai, Xiaofei He, and Xian-Sheng Hua. 2020. MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.

[47]

Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In The 27th International Joint Conference on Artificial Intelligence.

[48]

Q. Zhang, J. Chang, G. Meng, S. Xiang, and C. Pan. 2020. Spatio-Temporal Graph Structure Learning for Traffic Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence.

[49]

Siyuan Zhao and Neil Heffernan. 2017. Estimating Individual Treatment Effect from Educational Studies with Residual Counterfactual Networks. In Proceedings of 10th International Conference on Educational Data Mining.

[50]

Fangfang Zheng, Henk van Zuylen, and Xiaobo Liu. 2017. A Methodological Framework of Travel Time Distribution Estimation for Urban Signalized Arterial Roads. Transportation Science 51, 3 (2017), 893--917.

Digital Library

Cited By

Fujii KTakeuchi KKuribayashi ATakeishi NKawahara YTakeda K(2025)Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent ScenariosIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336116636:2(2103-2117)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3361166
Takeda MOnishi M(2023)Generating large-scale human flow datasets from measured pedestrian movement data and simulation2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386284(4026-4030)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386284

Index Terms

Causal Effect Estimation on Hierarchical Spatial Graph Data
1. Applied computing
  1. Operations research
    1. Forecasting

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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 04 August 2023

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JST PRESTO

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

Fujii KTakeuchi KKuribayashi ATakeishi NKawahara YTakeda K(2025)Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent ScenariosIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336116636:2(2103-2117)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3361166
Takeda MOnishi M(2023)Generating large-scale human flow datasets from measured pedestrian movement data and simulation2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386284(4026-4030)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386284

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