Discovering causal relationships between different variables from time series
data has been a long-standing challenge for many domains such as climate
science, finance, and healthcare. Given the complexity of real-world
relationships and the nature of observations in discrete time, causal discovery
methods need to consider non-linear relations between variables, instantaneous
effects and history-dependent noise (the change of noise distribution due to
past actions). However, previous works do not offer a solution addressing all
these problems together. In this paper, we propose a novel causal relationship
learning framework for time-series data, called Rhino, which combines vector
auto-regression, deep learning and variational inference to model non-linear
relationships with instantaneous effects while allowing the noise distribution
to be modulated by historical observations. Theoretically, we prove the
structural identifiability of Rhino. Our empirical results from extensive
synthetic experiments and two real-world benchmarks demonstrate better
discovery performance compared to relevant baselines, with ablation studies
revealing its robustness under model misspecification.

本文提出了一个新的因果关系理解框架 Rhino，通过向量自回归、深度学习和变分推断等方法，模拟非线性关系和瞬时影响，同时允许通过历史记录调制噪声分布，这种方法在综合合成实验和两项真实基准测试中均表现出比相关基线更好的性能。

Rhino: 带依赖历史噪声的深度因果时间关系学习

Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise

Distributed word vector spaces are considered hard to interpret which hinders
the understanding of natural language processing (NLP) models. In this work, we
introduce a new method to interpret arbitrary samples from a word vector space.
To this end, we train a neural model to conceptualize word vectors, which means
that it activates higher order concepts it recognizes in a given vector.
Contrary to prior approaches, our model operates in the original vector space
and is capable of learning non-linear relations between word vectors and
concepts. Furthermore, we show that it produces considerably less entropic
concept activation profiles than the popular cosine similarity.

本文提出了一种新的方法来解释任意分布词向量空间的样本，它训练了一个神经模型来生成高级别概念，并且能够在原始向量空间中学习非线性关系。同时，它产生的激活轮廓比流行的余弦相似性少得多。

词向量空间解释的神经向量概念化

Neural Vector Conceptualization for Word Vector Space Interpretation

We address the problem of causal discovery from data, making use of the
recently proposed causal modeling framework of modular structural causal models
(mSCM) to handle cycles, latent confounders and non-linearities. We introduce
{\sigma}-connection graphs ({\sigma}-CG), a new class of mixed graphs
(containing undirected, bidirected and directed edges) with additional
structure, and extend the concept of {\sigma}-separation, the appropriate
generalization of the well-known notion of d-separation in this setting, to
apply to {\sigma}-CGs. We prove the closedness of {\sigma}-separation under
marginalisation and conditioning and exploit this to implement a test of
{\sigma}-separation on a {\sigma}-CG. This then leads us to the first causal
discovery algorithm that can handle non-linear functional relations, latent
confounders, cyclic causal relationships, and data from different (stochastic)
perfect interventions. As a proof of concept, we show on synthetic data how
well the algorithm recovers features of the causal graph of modular structural
causal models.

采用模块化结构因果模型 (mSCM)，引入了 sigma-connection graphs (sigma-CG)，成功实现了能够处理非线性功能关系、潜在混淆、循环因果关系和不同随机完美干预数据的因果发现算法。