It has been said, arguably, that causality analysis should pave a promising
way to interpretable deep learning and generalization. Incorporation of
causality into artificial intelligence (AI) algorithms, however, is challenged
with its vagueness, non-quantitiveness, computational inefficiency, etc. During
the past 18 years, these challenges have been essentially resolved, with the
establishment of a rigorous formalism of causality analysis initially motivated
from atmospheric predictability. This not only opens a new field in the
atmosphere-ocean science, namely, information flow, but also has led to
scientific discoveries in other disciplines, such as quantum mechanics,
neuroscience, financial economics, etc., through various applications. This
note provides a brief review of the decade-long effort, including a list of
major theoretical results, a sketch of the causal deep learning framework, and
some representative real-world applications in geoscience pertaining to this
journal, such as those on the anthropogenic cause of global warming, the
decadal prediction of El Ni\~no Modoki, the forecasting of an extreme drought
in China, among others.

因果分析对于可解释的深度学习和泛化提供了有前景的途径，通过将因果性纳入人工智能算法，并利用气象可预测性初次构建严格的因果分析形式体系，在过去的 18 年中，成功解决了以往因果性分析中模糊、非数量化、计算效率低等挑战，并通过广泛应用在大气海洋科学以及其他学科领域，如量子力学、神经科学、金融经济学等，带来了科学发现。本文简要回顾了这十年来的探索工作，包括重要的理论结果列表、因果深度学习框架的概述以及与地球科学相关的一些实际应用，例如关于全球变暖人为原因、El Niño Modoki 的年代预测、中国极端干旱的预测等。

定量因果关系、因果引导的科学发现和因果机器学习

Quantitative causality, causality-guided scientific discovery, and  causal machine learning

While code generation has been widely used in various software development
scenarios, the quality of the generated code is not guaranteed. This has been a
particular concern in the era of large language models (LLMs)- based code
generation, where LLMs, deemed a complex and powerful black-box model, is
instructed by a high-level natural language specification, namely a prompt, to
generate code. Nevertheless, effectively evaluating and explaining the code
generation capability of LLMs is inherently challenging, given the complexity
of LLMs and the lack of transparency.
Inspired by the recent progress in causality analysis and its application in
software engineering, this paper launches a causality analysis-based approach
to systematically analyze the causal relations between the LLM input prompts
and the generated code. To handle various technical challenges in this study,
we first propose a novel causal graph-based representation of the prompt and
the generated code, which is established over the fine-grained,
human-understandable concepts in the input prompts. The formed causal graph is
then used to identify the causal relations between the prompt and the derived
code. We illustrate the insights that our framework can provide by studying
over 3 popular LLMs with over 12 prompt adjustment strategies. The results of
these studies illustrate the potential of our technique to provide insights
into LLM effectiveness, and aid end-users in understanding predictions.
Additionally, we demonstrate that our approach provides actionable insights to
improve the quality of the LLM-generated code by properly calibrating the
prompt.

基于因果分析的方法用于系统地分析大语言模型（LLMs）输入提示与生成代码之间的因果关系，研究结果展示了该技术在提供 LLMs 有效性方面的潜力，并帮助最终用户理解预测，同时提供改进 LLMs 生成代码质量的可操作见解。

基于大型语言模型的代码生成的基准测试与解释：一种因果关系为中心的方法

Benchmarking and Explaining Large Language Model-based Code Generation:  A Causality-Centric Approach

There has been an increasing interest in enhancing the fairness of machine
learning (ML). Despite the growing number of fairness-improving methods, we
lack a systematic understanding of the trade-offs among factors considered in
the ML pipeline when fairness-improving methods are applied. This understanding
is essential for developers to make informed decisions regarding the provision
of fair ML services. Nonetheless, it is extremely difficult to analyze the
trade-offs when there are multiple fairness parameters and other crucial
metrics involved, coupled, and even in conflict with one another.
This paper uses causality analysis as a principled method for analyzing
trade-offs between fairness parameters and other crucial metrics in ML
pipelines. To ractically and effectively conduct causality analysis, we propose
a set of domain-specific optimizations to facilitate accurate causal discovery
and a unified, novel interface for trade-off analysis based on well-established
causal inference methods. We conduct a comprehensive empirical study using
three real-world datasets on a collection of widelyused fairness-improving
techniques. Our study obtains actionable suggestions for users and developers
of fair ML. We further demonstrate the versatile usage of our approach in
selecting the optimal fairness-improving method, paving the way for more
ethical and socially responsible AI technologies.

本论文使用因果分析作为分析机器学习中公平性参数和其他重要指标的权衡的原则方法，并在广泛使用的公平性改进技术上进行了全面的实证研究，提供了可操作的建议，以及在选择最佳公平性改进方法方面提供了多种可能性。