We present a framework for large language model (LLM) based data generation with controllable causal structure. In particular, we define a procedure for turning any language model and any directed acyclic graph (DAG) into a sequence-driven structural causal model (SD-SCM). Broadly speaking, an SD-SCM is a causal model with user-defined structure and LLM-defined structural equations. We characterize how an SD-SCM allows sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. We then leverage this procedure to propose a new type of benchmark for causal inference methods, generating individual-level counterfactual data without needing to manually specify functional relationships between variables. We create an example benchmark consisting of thousands of datasets, and test a suite of popular estimation methods on these datasets for average, conditional average, and individual treatment effect estimation, both with and without hidden confounding. Apart from generating data, the same procedure also allows us to test for the presence of a causal effect that might be encoded in an LLM. This procedure can underpin auditing LLMs for misinformation, discrimination, or otherwise undesirable behavior. We believe SD-SCMs can serve as a useful tool in any application that would benefit from sequential data with controllable causal structure.

本研究提出了一种基于大型语言模型（LLM）的数据生成框架，能够控制因果结构。通过将任何语言模型与有向无环图（DAG）结合，形成顺序驱动的结构性因果模型（SD-SCM），研究展示了如何根据所需的因果结构从观察、干预和反事实分布中进行取样。这一方法不仅为因果推断方法提供了新的基准，还能有效检测语言模型中可能编码的因果效应，对解决错误信息和歧视等问题具有潜在影响。

语言模型作为因果效应生成器