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.
翻译:在多个工程和科学领域,当人应该干预自主驾驶系统,当一个玩者应该为好镜头而将球员传给队友,在各种工程和科学领域都具有挑战性。利用反事实长期预测来估计个别治疗效应(ITE)是实际评估此类干预措施的实用方法。然而,大多数常规框架没有考虑到多试系统关系和共变反事实预测的复杂时间变化结构,有时这可能导致对ITE和解释问题的错误评估。我们在这里提议多试系统的一个可解释的、反事实的经常性网络,以估计干预的效果。我们的模型利用基于对反事实长期预测的长期长期预测的个别治疗效应(IT),用对反事实长期长期预测的长远长期预测来估计干预干预措施的个别治疗效应(ITE评估框架)来估计个别治疗效应(ITE评估框架的域知识,这种预测在干预有效的情况下可以证实,但是,大多数常规框架没有考虑到多试聚合变异性和结果的长期预测的复杂结构,但是大多数常规框架没有考虑到多试关系和共试关系和共变换反反反反变反变反变反变反反预测的复合装置和反变反变反变反变反变反变反变反预测的复合预测的复杂预测的复杂预测的复合物剂和生物剂的模拟模型模型模型的复杂预测模型模型模型模型模型模型模型模型模型模型模型模型模型模型模型模型模型模型,我们的方法方法在我们的方法方法下,我们的方法在反现实变的反变的反变计算和最低变变变变计算和最低算和最少估计的反变的计算中,我们的方法方法在反常变差变差变的反变变变变差变差和最有效的实际和反变变变变差和最有效的治疗、在反变的计算和最有效的治疗时算和在反变的预测中得出性、反变时算和最有效的治疗时算和最晚现实和最现实的预测时算、反变时算时算时算时算时算时差时差时算流流流流流流流流流流流流流流流流流流流流流流流流流流流时,我们的方法中,我们的方法在计算时算和生物测测测测测时算的计算中,我们方法中,我们的计算的计算方法的计算