Related papers: Relational Structural Causal Models

Relational Structural Causal Models

URL: http://arxiv.org/abs/2606.14892v1
Date: Fri, 12 Jun 2026 18:54:06 GMT
Title: Relational Structural Causal Models
Abstract summary: We develop relational structural causal models, extending structural causal models to settings where objects and their relations vary.<n>We propose relational neural causal models, a provably correct approach that outperforms non-relational baselines on simulated traffic scenes.
Score: 52.818153111470394
License: http://creativecommons.org/licenses/by/4.0/
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.

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