Relational Structural Causal Models

Abstract

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.