  Evaluation of intervention in a multiagent 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 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 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.
