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Estimating counterfactual treatment outcomes over time in multi-vehicle simulation

Published: 22 November 2022 Publication History

Abstract

Evaluation of intervention in a multi-agent system, e.g., when humans should intervene in autonomous driving systems, is challenging in various engineering and scientific fields. Estimating the individual treatment effect (ITE) using counterfactual long-term prediction is practical to evaluate such interventions. However, most of the conventional frameworks did not consider the time-varying complex structure of multi-agent relationships and covariate counterfactual prediction. Here we propose an interpretable, counterfactual recurrent network in multi-agent systems to estimate the effect of the intervention. Our model leverages graph variational recurrent neural networks and theory-based computation with domain knowledge for the ITE estimation framework based on long-term prediction of multi-agent covariates and outcomes, which can confirm the circumstances under which the intervention is effective. On simulated models of an automated vehicle with time-varying confounders, we show that our methods achieved lower estimation errors in counterfactual covariates.

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Cited By

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  • (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
  • (2023)Estimating the effect of hitting strategies in baseball using counterfactual virtual simulation with deep learningInternational Journal of Computer Science in Sport10.2478/ijcss-2023-000122:1(1-12)Online publication date: 17-Jan-2023
  • (2023)Pitching strategy evaluation via stratified analysis using propensity scoreJournal of Quantitative Analysis in Sports10.1515/jqas-2021-006019:2(91-102)Online publication date: 18-May-2023
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        cover image ACM Conferences
        SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
        November 2022
        806 pages
        ISBN:9781450395298
        DOI:10.1145/3557915
        Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Publication History

        Published: 22 November 2022

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        Author Tags

        1. autonomous vehicle
        2. causal inference
        3. deep generative model
        4. multi-agent modeling
        5. trajectory data

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        • JSPS KAKENHI
        • JST CREST
        • JST PRESTO

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        Overall Acceptance Rate 257 of 1,238 submissions, 21%

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        View all
        • (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
        • (2023)Estimating the effect of hitting strategies in baseball using counterfactual virtual simulation with deep learningInternational Journal of Computer Science in Sport10.2478/ijcss-2023-000122:1(1-12)Online publication date: 17-Jan-2023
        • (2023)Pitching strategy evaluation via stratified analysis using propensity scoreJournal of Quantitative Analysis in Sports10.1515/jqas-2021-006019:2(91-102)Online publication date: 18-May-2023
        • (2023)CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical SystemsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599272(997-1009)Online publication date: 6-Aug-2023
        • (2023)Causal Effect Estimation on Hierarchical Spatial Graph DataProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599269(2145-2154)Online publication date: 6-Aug-2023
        • (2023)Evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory PredictionMachine Learning and Data Mining for Sports Analytics10.1007/978-3-031-27527-2_5(53-73)Online publication date: 25-Feb-2023

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