Spatiotemporal time series is the foundation of understanding human
activities and their impacts, which is usually collected via monitoring sensors
placed at different locations. The collected data usually contains missing
values due to various failures, which have significant impact on data analysis.
To impute the missing values, a lot of methods have been introduced. When
recovering a specific data point, most existing methods tend to take into
consideration all the information relevant to that point regardless of whether
they have a cause-and-effect relationship. During data collection, it is
inevitable that some unknown confounders are included, e.g., background noise
in time series and non-causal shortcut edges in the constructed sensor network.
These confounders could open backdoor paths between the input and output, in
other words, they establish non-causal correlations between the input and
output. Over-exploiting these non-causal correlations could result in
overfitting and make the model vulnerable to noises. In this paper, we first
revisit spatiotemporal time series imputation from a causal perspective, which
shows the causal relationships among the input, output, embeddings and
confounders. Next, we show how to block the confounders via the frontdoor
adjustment. Based on the results of the frontdoor adjustment, we introduce a
novel Causality-Aware SPatiotEmpoRal graph neural network (CASPER), which
contains a novel Spatiotemporal Causal Attention (SCA) and a Prompt Based
Decoder (PBD). PBD could reduce the impact of confounders and SCA could
discover the sparse causal relationships among embeddings. Theoretical analysis
reveals that SCA discovers causal relationships based on the values of
gradients. We evaluate Casper on three real-world datasets, and the
experimental results show that Casper outperforms the baselines and effectively
discovers causal relationships.

从因果的角度重新审视了时空时间序列的填充，引入了前门调整与一个基于因果感知的时空图神经网络 (CASPER)，它包含了一个新颖的时空因果注意力 (SCA) 和一个基于提示的解码器 (PBD)。PBD 可以减少混淆变量的影响，而 SCA 可以发现嵌入之间的稀疏因果关系。经过理论分析，发现 SCA 是基于梯度值来发现因果关系的。通过在三个真实数据集上的评估，实验证明 Casper 能够胜过基准方法，并有效地发现因果关系。

CASPER: 因果感知的时空图神经网络对时空时间序列插值

CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for  Spatiotemporal Time Series Imputation

Discovering causal structure from purely observational data (i.e., causal
discovery), aiming to identify causal relationships among variables, is a
fundamental task in machine learning. The recent invention of differentiable
score-based DAG learners is a crucial enabler, which reframes the combinatorial
optimization problem into a differentiable optimization with a DAG constraint
over directed graph space. Despite their great success, these cutting-edge DAG
learners incorporate DAG-ness independent score functions to evaluate the
directed graph candidates, lacking in considering graph structure. As a result,
measuring the data fitness alone regardless of DAG-ness inevitably leads to
discovering suboptimal DAGs and model vulnerabilities. Towards this end, we
propose a dynamic causal space for DAG structure learning, coined CASPER, that
integrates the graph structure into the score function as a new measure in the
causal space to faithfully reflect the causal distance between estimated and
ground truth DAG. CASPER revises the learning process as well as enhances the
DAG structure learning via adaptive attention to DAG-ness. Grounded by
empirical visualization, CASPER, as a space, satisfies a series of desired
properties, such as structure awareness and noise robustness. Extensive
experiments on both synthetic and real-world datasets clearly validate the
superiority of our CASPER over the state-of-the-art causal discovery methods in
terms of accuracy and robustness.

提出了一个新的因果结构学习动态因果空间（CASPER），能够整合图结构作为因果空间中的新度量，以准确反映估计和真实 DAG 之间的因果距离，并通过自适应关注 DAG-ness 增强 DAG 结构学习。

发现动态因果空间进行有向无环图结构学习

Discovering Dynamic Causal Space for DAG Structure Learning

Generating counterfactual test-cases is an important backbone for testing NLP
models and making them as robust and reliable as traditional software. In
generating the test-cases, a desired property is the ability to control the
test-case generation in a flexible manner to test for a large variety of
failure cases and to explain and repair them in a targeted manner. In this
direction, significant progress has been made in the prior works by manually
writing rules for generating controlled counterfactuals. However, this approach
requires heavy manual supervision and lacks the flexibility to easily introduce
new controls. Motivated by the impressive flexibility of the plug-and-play
approach of PPLM, we propose bringing the framework of plug-and-play to
counterfactual test case generation task. We introduce CASPer, a plug-and-play
counterfactual generation framework to generate test cases that satisfy goal
attributes on demand. Our plug-and-play model can steer the test case
generation process given any attribute model without requiring
attribute-specific training of the model. In experiments, we show that CASPer
effectively generates counterfactual text that follow the steering provided by
an attribute model while also being fluent, diverse and preserving the original
content. We also show that the generated counterfactuals from CASPer can be
used for augmenting the training data and thereby fixing and making the test
model more robust.

CASPer 框架使用插入即用的方法生成能够满足目标属性的对抗文本，用于补充训练数据，从而提高测试模型的鲁棒性和可靠性。

插拔式因果文本生成：提升模型鲁棒性

Plug and Play Counterfactual Text Generation for Model Robustness

We propose CASPER (ChAt, Shift and PERform), a novel dialog system consisting
of three types of dialog models: chatter, shifter, and performer. Shifter,
which is designed for topic switching, enables a seamless flow of dialog from
open-domain chat- to task-oriented dialog. In a user study, CASPER gave a
better impression in terms of naturalness of response, lack of forced topic
switching, and satisfaction compared with a baseline dialog system trained in
an end-to-end manner. In an ablation study, we found that naturalness of
response, dialog satisfaction, and task-elicitation rate improved compared with
when shifter was removed from CASPER, indicating that topic shift with shifter
supports the introduction of natural task-oriented dialog.

CASPER 是一种新型的对话系统，包含三种对话模型：聊天机器人，主题转换机器人和执行机器人。在用户研究中，相比于端到端训练的基线对话系统，CASPER 在自然度、无强制主题转换和用户满意度等方面给人留下了更好的印象。在消融研究中，我们发现，与没有主题转换机器人的情况相比，带有主题转换机器人的 CASPER 具有更好的自然度、对话满意度和任务引发率，表明具有 Shifter 的主题转换支持引入了自然的任务导向对话。