Social science research often hinges on the relationship between categorical
variables and outcomes. We introduce CAVIAR, a novel method for embedding
categorical variables that assume values in a high-dimensional ambient space
but are sampled from an underlying manifold. Our theoretical and numerical
analyses outline challenges posed by such categorical variables in causal
inference. Specifically, dynamically varying and sparse levels can lead to
violations of the Donsker conditions and a failure of the estimation
functionals to converge to a tight Gaussian process. Traditional approaches,
including the exclusion of rare categorical levels and principled variable
selection models like LASSO, fall short. CAVIAR embeds the data into a
lower-dimensional global coordinate system. The mapping can be derived from
both structured and unstructured data, and ensures stable and robust estimates
through dimensionality reduction. In a dataset of direct-to-consumer apparel
sales, we illustrate how high-dimensional categorical variables, such as zip
codes, can be succinctly represented, facilitating inference and analysis.

通过嵌入方法 CAVIAR，将高维分类变量嵌入到较低维度的全局坐标系中，从而稳定可靠地估计减少维度后的数据，促进推断和分析。

CAVIAR: 精确和稳健推理的分类变量嵌入

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust  Inference

Digital twins are an important technology for advancing mobile
communications, specially in use cases that require simultaneously simulating
the wireless channel, 3D scenes and machine learning. Aiming at providing a
solution to this demand, this work describes a modular co-simulation
methodology called CAVIAR. Here, CAVIAR is upgraded to support a message
passing library and enable the virtual counterpart of a digital twin system
using different 6G-related simulators. The main contributions of this work are
the detailed description of different CAVIAR architectures, the implementation
of this methodology to assess a 6G use case of UAV-based search and rescue
mission (SAR), and the generation of benchmarking data about the computational
resource usage. For executing the SAR co-simulation we adopt five open-source
solutions: the physical and link level network simulator Sionna, the simulator
for autonomous vehicles AirSim, scikit-learn for training a decision tree for
MIMO beam selection, Yolov8 for the detection of rescue targets and NATS for
message passing. Results for the implemented SAR use case suggest that the
methodology can run in a single machine, with the main demanded resources being
the CPU processing and the GPU memory.

CAVIAR 是一种模块化的共模拟方法，用于支持数字孪生系统的虚拟对应物，并使用不同的与 6G 相关的模拟器评估 6G 应用，主要关注了使用开源解决方案模拟救援任务的性能和资源使用数据。