Causal structure discovery from observations can be improved by integrating
background knowledge provided by an expert to reduce the hypothesis space.
Recently, Large Language Models (LLMs) have begun to be considered as sources
of prior information given the low cost of querying them relative to a human
expert. In this work, firstly, we propose a set of metrics for assessing LLM
judgments for causal graph discovery independently of the downstream algorithm.
Secondly, we systematically study a set of prompting designs that allows the
model to specify priors about the structure of the causal graph. Finally, we
present a general methodology for the integration of LLM priors in graph
discovery algorithms, finding that they help improve performance on
common-sense benchmarks and especially when used for assessing edge
directionality. Our work highlights the potential as well as the shortcomings
of the use of LLMs in this problem space.

整合专家提供的背景知识可以改善观测数据的因果结构发现，并且由于对比人工专家而言查询它们的成本较低，最近开始将大型语言模型（LLM）视为提供先验信息的来源。本研究首先提出了一组用于独立于下游算法评估 LLM 判断因果图发现的度量标准。其次，我们系统地研究了一组提示设计，使模型能够指定关于因果图结构的先验。最后，我们提出了一种将 LLM 先验整合到图发现算法中的通用方法，发现它们在常识基准和特别是用于评估边缘方向性时有助于提高性能。我们的研究突显了在该问题空间中使用 LLM 的潜力和局限性。

大型语言模型对因果图发现的有效先验

Large Language Models are Effective Priors for Causal Graph Discovery

Causal graph discovery is a significant problem with applications across
various disciplines. However, with observational data alone, the underlying
causal graph can only be recovered up to its Markov equivalence class, and
further assumptions or interventions are necessary to narrow down the true
graph. This work addresses the causal discovery problem under the setting of
stochastic interventions with the natural goal of minimizing the number of
interventions performed. We propose the following stochastic intervention model
which subsumes existing adaptive noiseless interventions in the literature
while capturing scenarios such as fat-hand interventions and CRISPR gene
knockouts: any intervention attempt results in an actual intervention on a
random subset of vertices, drawn from a distribution dependent on attempted
action. Under this model, we study the two fundamental problems in causal
discovery of verification and search and provide approximation algorithms with
polylogarithmic competitive ratios and provide some preliminary experimental
results.

该研究论文提出了一种基于随机干预的因果发现模型，通过最小化干预次数来解决因果发现问题，提供了多对数竞争比的近似算法，并给出了一些初步实验结果。

非目标干预下的因果发现

Causal Discovery under Off-Target Interventions

We propose a novel framework that leverages LLMs for full causal graph
discovery. While previous LLM-based methods have used a pairwise query
approach, this requires a quadratic number of queries which quickly becomes
impractical for larger causal graphs. In contrast, the proposed framework uses
a breadth-first search (BFS) approach which allows it to use only a linear
number of queries. We also show that the proposed method can easily incorporate
observational data when available, to improve performance. In addition to being
more time and data-efficient, the proposed framework achieves state-of-the-art
results on real-world causal graphs of varying sizes. The results demonstrate
the effectiveness and efficiency of the proposed method in discovering causal
relationships, showcasing its potential for broad applicability in causal graph
discovery tasks across different domains.

提出了一种利用 LLMs 进行全因果图发现的新框架，通过广度优先搜索（BFS）方法，使用线性数量的查询，可在不同领域的因果图发现任务中获得具有前沿水平结果的高效性和效果。

利用大语言模型高效发现因果图

Efficient Causal Graph Discovery Using Large Language Models

Recovering causal relationships from data is an important problem. Using
observational data, one can typically only recover causal graphs up to a Markov
equivalence class and additional assumptions or interventional data are needed
for complete recovery. In this work, under some standard assumptions, we study
causal graph discovery via adaptive interventions with node-dependent
interventional costs. For this setting, we show that no algorithm can achieve
an approximation guarantee that is asymptotically better than linear in the
number of vertices with respect to the verification number; a well-established
benchmark for adaptive search algorithms. Motivated by this negative result, we
define a new benchmark that captures the worst-case interventional cost for any
search algorithm. Furthermore, with respect to this new benchmark, we provide
adaptive search algorithms that achieve logarithmic approximations under
various settings: atomic, bounded size interventions and generalized cost
objectives.

通过研究自适应干预下的因果图发现，提出了一种新的基准，捕捉任何搜索算法的最坏干预成本，并提出了在各种设置下实现对数逼近的自适应搜索算法。