In practical statistical causal discovery (SCD), embedding domain expert
knowledge as constraints into the algorithm is widely accepted as significant
for creating consistent meaningful causal models, despite the recognized
challenges in systematic acquisition of the background knowledge. To overcome
these challenges, this paper proposes a novel methodology for causal inference,
in which SCD methods and knowledge based causal inference (KBCI) with a large
language model (LLM) are synthesized through "statistical causal prompting
(SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have
revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result
with prior knowledge from LLM-KBCI to approach the ground truth, and that the
SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, it has
been clarified that an LLM can improve SCD with its background knowledge, even
if the LLM does not contain information on the dataset. The proposed approach
can thus address challenges such as dataset biases and limitations,
illustrating the potential of LLMs to improve data-driven causal inference
across diverse scientific domains.

通过将领域专家知识作为约束嵌入到算法中，结合使用统计因果推断方法和基于知识的因果推断方法，通过大型语言模型进行统计因果提示，可以改善数据驱动的因果推断。该方法能够解决数据集偏差和限制等挑战，展示了大型语言模型在不同科学领域中改进数据驱动因果推断的潜力。

在因果发现中整合大语言模型：统计因果方法

Integrating Large Language Models in Causal Discovery: A Statistical  Causal Approach

Identifying causal relationships from observational time series data is a key
problem in disciplines such as climate science or neuroscience, where
experiments are often not possible. Data-driven causal inference is challenging
since datasets are often high-dimensional and nonlinear with limited sample
sizes. Here we introduce a novel method that flexibly combines linear or
nonlinear conditional independence tests with a causal discovery algorithm that
allows to reconstruct causal networks from large-scale time series datasets. We
validate the method on a well-established climatic teleconnection connecting
the tropical Pacific with extra-tropical temperatures and using large-scale
synthetic datasets mimicking the typical properties of real data. The
experiments demonstrate that our method outperforms alternative techniques in
detection power from small to large-scale datasets and opens up entirely new
possibilities to discover causal networks from time series across a range of
research fields.

提出了一种结合线性或非线性条件独立性检验与因果发现算法的方法，可以从大规模时间序列数据集中重构因果网络，从而在气候科学或神经科学等研究领域中识别因果关系。