Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios

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 lead to erroneous assessments of ITE …

agent autonomous autonomous driving autonomous driving systems counterfactual cs.ai cs.lg cs.ma driving engineering evaluation fields good humans long-term multi-agent practical prediction stat.me stat.ml systems treatment