An artificial intelligence must have a model of its environment that is causal, supporting reasoning about interventions and counterfactuals, and also combinatorial, supporting generalization to unseen combinations of objects. In this work, we formally study when and how such a model can be learned. We develop relational structural causal models, extending structural causal models (Pearl 2009) to settings where objects and their relations vary. First, we show how answers to not only causal but also observational queries about unseen combinations of objects can not be identified without further assumptions. To enable such identification--including in the presence of unobserved confounding--we define relational causal graphs and derive symbolic identification criteria. Finally, we propose relational neural causal models, a provably correct approach that outperforms non-relational baselines on simulated traffic scenes with varying cars, signals, and pedestrians.

翻译：人工智能必须拥有其环境的因果模型，既支持关于干预和反事实的推理，又具备组合性，能够泛化到未见过的物体组合。在这项工作中，我们正式研究了何时以及如何学习这样的模型。我们发展了关系结构因果模型，将结构因果模型（Pearl 2009）扩展到物体及其关系变化的场景。首先，我们展示了如果没有进一步假设，不仅因果查询，而且关于未见物体组合的观测查询的答案也无法被识别。为了实现这种识别——包括在存在未观测混淆的情况下——我们定义了关系因果图并推导出符号识别准则。最后，我们提出了关系神经因果模型，这是一种可证明正确的方法，在包含可变车辆、信号和行人的模拟交通场景中优于非关系基线方法。