Science originates with discovering new causal knowledge from a combination
of known facts and observations. Traditional causal discovery approaches mainly
rely on high-quality measured variables, usually given by human experts, to
find causal relations. However, the causal variables are usually unavailable in
a wide range of real-world applications. The rise of large language models
(LLMs) that are trained to learn rich knowledge from the massive observations
of the world, provides a new opportunity to assist with discovering high-level
hidden variables from the raw observational data. Therefore, we introduce COAT:
Causal representatiOn AssistanT. COAT incorporates LLMs as a factor proposer
that extracts the potential causal factors from unstructured data. Moreover,
LLMs can also be instructed to provide additional information used to collect
data values (e.g., annotation criteria) and to further parse the raw
unstructured data into structured data. The annotated data will be fed to a
causal learning module (e.g., the FCI algorithm) that provides both rigorous
explanations of the data, as well as useful feedback to further improve the
extraction of causal factors by LLMs. We verify the effectiveness of COAT in
uncovering the underlying causal system with two case studies of review rating
analysis and neuropathic diagnosis.

COAT utilizes large language models to extract potential causal factors from unstructured data and assists in uncovering underlying causal systems.

利用大型语言模型探索隐藏世界

Discovery of the Hidden World with Large Language Models

Existing methods of multiple human parsing (MHP) apply statistical models to
acquire underlying associations between images and labeled body parts. However,
acquired associations often contain many spurious correlations that degrade
model generalization, leading statistical models to be vulnerable to visually
contextual variations in images (e.g., unseen image styles/external
interventions). To tackle this, we present a causality inspired parsing
paradigm termed CIParsing, which follows fundamental causal principles
involving two causal properties for human parsing (i.e., the causal diversity
and the causal invariance). Specifically, we assume that an input image is
constructed by a mix of causal factors (the characteristics of body parts) and
non-causal factors (external contexts), where only the former ones cause the
generation process of human parsing.Since causal/non-causal factors are
unobservable, a human parser in proposed CIParsing is required to construct
latent representations of causal factors and learns to enforce representations
to satisfy the causal properties. In this way, the human parser is able to rely
on causal factors w.r.t relevant evidence rather than non-causal factors w.r.t
spurious correlations, thus alleviating model degradation and yielding improved
parsing ability. Notably, the CIParsing is designed in a plug-and-play fashion
and can be integrated into any existing MHP models. Extensive experiments
conducted on two widely used benchmarks demonstrate the effectiveness and
generalizability of our method.

通过引入因果关系理论，提出了一种 CIParsing 人体解析模型，通过构建潜在因果因素的表示来改善多人解析模型的泛化能力。

CIParsing: 将因果属性统一到多个人的解析中

CIParsing: Unifying Causality Properties into Multiple Human Parsing

Domain generalization (DG) is essentially an out-of-distribution problem,
aiming to generalize the knowledge learned from multiple source domains to an
unseen target domain. The mainstream is to leverage statistical models to model
the dependence between data and labels, intending to learn representations
independent of domain. Nevertheless, the statistical models are superficial
descriptions of reality since they are only required to model dependence
instead of the intrinsic causal mechanism. When the dependence changes with the
target distribution, the statistic models may fail to generalize. In this
regard, we introduce a general structural causal model to formalize the DG
problem. Specifically, we assume that each input is constructed from a mix of
causal factors (whose relationship with the label is invariant across domains)
and non-causal factors (category-independent), and only the former cause the
classification judgments. Our goal is to extract the causal factors from inputs
and then reconstruct the invariant causal mechanisms. However, the theoretical
idea is far from practical of DG since the required causal/non-causal factors
are unobserved. We highlight that ideal causal factors should meet three basic
properties: separated from the non-causal ones, jointly independent, and
causally sufficient for the classification. Based on that, we propose a
Causality Inspired Representation Learning (CIRL) algorithm that enforces the
representations to satisfy the above properties and then uses them to simulate
the causal factors, which yields improved generalization ability. Extensive
experimental results on several widely used datasets verify the effectiveness
of our approach.

本文提出了一种基于因果关系的结构性因果模型来解决域泛化问题，并基于此提出了一种 Causality Inspired Representation Learning （CIRL）算法，该算法通过计算需满足的三个基本属性来强制表示满足因果机制的性质，进而提升了广义性能力。实验结果表明，该方法有效。

因果启发式表示学习用于领域泛化

Causality Inspired Representation Learning for Domain Generalization

This paper elaborates how to identify and evaluate causal factors to improve
scientific impact. Currently, analyzing scientific impact can be beneficial to
various academic activities including funding application, mentor
recommendation, and discovering potential cooperators etc. It is universally
acknowledged that high-impact scholars often have more opportunities to receive
awards as an encouragement for their hard working. Therefore, scholars spend
great efforts in making scientific achievements and improving scientific impact
during their academic life. However, what are the determinate factors that
control scholars' academic success? The answer to this question can help
scholars conduct their research more efficiently. Under this consideration, our
paper presents and analyzes the causal factors that are crucial for scholars'
academic success. We first propose five major factors including
article-centered factors, author-centered factors, venue-centered factors,
institution-centered factors, and temporal factors. Then, we apply recent
advanced machine learning algorithms and jackknife method to assess the
importance of each causal factor. Our empirical results show that
author-centered and article-centered factors have the highest relevancy to
scholars' future success in the computer science area. Additionally, we
discover an interesting phenomenon that the h-index of scholars within the same
institution or university are actually very close to each other.

本论文探讨了科研影响力的关键控制因素并借助机器学习和 Jackknife 方法进行实证分析，结果发现文章中心因素和作者中心因素对计算机科学领域的学者未来的学术成功最具关联性。