  To perform effective causal inference in high-dimensional datasets,
initiating the process with causal discovery is imperative, wherein a causal
graph is generated based on observational data. However, obtaining a complete
and accurate causal graph poses a formidable challenge, recognized as an
NP-hard problem. Recently, the advent of Large Language Models (LLMs) has
ushered in a new era, indicating their emergent capabilities and widespread
applicability in facilitating causal reasoning across diverse domains, such as
medicine, finance, and science. The expansive knowledge base of LLMs holds the
potential to elevate the field of causal reasoning by offering
interpretability, making inferences, generalizability, and uncovering novel
causal structures. In this paper, we introduce a new framework, named
Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize
data-driven causal discovery algorithms and LLMs, automating the generation of
a more resilient, accurate, and explicable causal graph. The ALCM consists of
three integral components: causal structure learning, causal wrapper, and
LLM-driven causal refiner. These components autonomously collaborate within a
dynamic environment to address causal discovery questions and deliver plausible
causal graphs. We evaluate the ALCM framework by implementing two
demonstrations on seven well-known datasets. Experimental results demonstrate
that ALCM outperforms existing LLM methods and conventional data-driven causal
reasoning mechanisms. This study not only shows the effectiveness of the ALCM
but also underscores new research directions in leveraging the causal reasoning
capabilities of LLMs.
