Causal inference is a fundamental research topic for discovering the
cause-effect relationships in many disciplines. However, not all algorithms are
equally well-suited for a given dataset. For instance, some approaches may only
be able to identify linear relationships, while others are applicable for
non-linearities. Algorithms further vary in their sensitivity to noise and
their ability to infer causal information from coupled vs. non-coupled time
series. Therefore, different algorithms often generate different causal
relationships for the same input. To achieve a more robust causal inference
result, this publication proposes a novel data-driven two-phase multi-split
causal ensemble model to combine the strengths of different causality base
algorithms. In comparison to existing approaches, the proposed ensemble method
reduces the influence of noise through a data partitioning scheme in the first
phase. To achieve this, the data are initially divided into several partitions
and the base algorithms are applied to each partition. Subsequently, Gaussian
mixture models are used to identify the causal relationships derived from the
different partitions that are likely to be valid. In the second phase, the
identified relationships from each base algorithm are then merged based on
three combination rules. The proposed ensemble approach is evaluated using
multiple metrics, among them a newly developed evaluation index for causal
ensemble approaches. We perform experiments using three synthetic datasets with
different volumes and complexity, which are specifically designed to test
causality detection methods under different circumstances while knowing the
ground truth causal relationships. In these experiments, our causality ensemble
outperforms each of its base algorithms. In practical applications, the use of
the proposed method could hence lead to more robust and reliable causality
results.

本文提出了一种基于数据驱动的两阶段多分割因果推断集成模型，结合了不同因果基本算法的优势，通过数据划分方案降低噪音影响。在多个指标的评估下，通过实验发现我们的因果集成方法在不同情况下均优于各个基本算法，可实现更强大和可靠的因果关系推断。

基于数据驱动的二阶段多重分割因果集合模型的时间序列

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time  Series

The paper describes the work that has been submitted to the 5th workshop on
Challenges and Applications of Automated Extraction of socio-political events
from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3
that aims to detect causality in protest news corpus. The authors used
different large language models with customized cross-entropy loss functions
that exploit annotation information. The experiments showed that
bert-based-uncased with refined cross-entropy outperformed the others,
achieving a F1 score of 0.8501 on the Causal News Corpus dataset.

该论文研究了利用基于 BERT 模型的自动化抽取技术进行抗议新闻因果关系检测的方法，提出了利用自定义的交叉熵损失函数的方法，实验结果表明 BERT-based-uncased 在 Causal News Corpus 数据集上的 F1 分数达到了 0.8501。

使用多注释决策进行因果检测

Causality Detection using Multiple Annotation Decisions

We present the FinCausal 2020 Shared Task on Causality Detection in Financial
Documents and the associated FinCausal dataset, and discuss the participating
systems and results. Two sub-tasks are proposed: a binary classification task
(Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted
runs across the two Tasks and 13 of them contributed with a system description
paper. This workshop is associated to the Joint Workshop on Financial Narrative
Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The
28th International Conference on Computational Linguistics (COLING'2020),
Barcelona, Spain on September 12, 2020.

介绍了 FinCausal 2020 共享任务和相关思路，对参与的系统和结果进行了讨论，该任务提供了两个子任务，即二元分类任务和关系抽取任务。共有 16 个团队参赛，在两个任务中提交了 runs，其中 13 个团队的系统描述论文做出了贡献。该研讨会与 “金融叙事处理和多语种金融摘要（FNP-FNS 2020）” 联合研讨会相关联，该会议于 2020 年 9 月 12 日在西班牙巴塞罗那举行，属于第 28 届国际计算语言学会议（COLING '2020）