Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios

Evaluation of intervention in a multiagent system, for example, when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields. Estimating the individual treatment effect (ITE) using c...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-02, Vol.PP, p.1-15
Main Authors: Fujii, Keisuke, Takeuchi, Koh, Kuribayashi, Atsushi, Takeishi, Naoya, Kawahara, Yoshinobu, Takeda, Kazuya
Format: Article
Language:English
Subjects:
Animals
Autonomous vehicle
Autonomous vehicles
Behavioral sciences
causal inference
deep generative model
Informatics
Multi-agent systems
multiagent modeling
Sports
Timing
trajectory data
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Evaluation of intervention in a multiagent system, for example, when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, 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 multiagent relationships and covariate counterfactual prediction. This may lead to erroneous assessments of ITE and difficulty in interpretation. Here, we propose an interpretable, counterfactual recurrent network in multiagent systems to estimate the effect of the intervention. Our model leverages graph variational recurrent neural networks (GVRNNs) and theory-based computation with domain knowledge for the ITE estimation framework based on long-term prediction of multiagent covariates and outcomes, which can confirm the circumstances under which the intervention is effective. On simulated models of an automated vehicle and biological agents with time-varying confounders, we show that our methods achieved lower estimation errors in counterfactual covariates and the most effective treatment timing than the baselines. Furthermore, using real basketball data, our methods performed realistic counterfactual predictions and evaluated the counterfactual passes in shot scenarios.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2024.3361166