Evaluation of intervention in a multi-agent system, e.g., 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 multi-agent relationships and covariate counterfactual prediction.
This may sometimes lead to erroneous assessments of ITE and interpretation
problems. 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
under 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.

使用可解释的多智能体系统反事实循环网络进行干预效应评估的长期预测方法，最终在自动驾驶、生物智能体和篮球计分方案的模拟实验与实际数据中证明了其优于传统方法的效果。