Scaling Causal Reasoning with Increasingly Complex Causal Simulators

Despite surpassing human performance across mathematics, coding, and other knowledge intensive tasks, large language models (LLMs) continue to struggle to causally reason. A core obstacle is the target data itself: causal systems are complex and often expressed in non-executable forms, and ground-truth answers to causal queries are inherently scarce. We introduce, CauSim, a framework that turns causal reasoning from a scarce-label problem into a scalable, supervised one. It constructs \textit{increasingly complex causal simulators}: executable structural causal models (SCMs), incrementally built by LLMs, that scale to globally complex systems while maintaining verifiable answers to any causal query. CauSim operates across representations by formalizing non-executable causal knowledge into code, allowing for data augmentation, and informalizing executable SCMs into natural language, enabling supervision in previously unsupervisable representations. We structure our research into two parts: (1) how to construct increasingly complex causal simulators, and (2) a systematic study of what CauSim enables, demonstrating generalization across representations, consistent gains from curriculum scaling and data volume, LLM self-improvement through self-generated simulators, and data augmentation via formalization of existing domain knowledge.