In this paper, we consider a setting where heterogeneous agents with
connectivity are performing inference using unlabeled streaming data. Observed
data are only partially informative about the target variable of interest. In
order to overcome the uncertainty, agents cooperate with each other by
exchanging their local inferences with and through a fusion center. To evaluate
how each agent influences the overall decision, we adopt a causal framework in
order to distinguish the actual influence of agents from mere correlations
within the decision-making process. Various scenarios reflecting different
agent participation patterns and fusion center policies are investigated. We
derive expressions to quantify the causal impact of each agent on the joint
decision, which could be beneficial for anticipating and addressing atypical
scenarios, such as adversarial attacks or system malfunctions. We validate our
theoretical results with numerical simulations and a real-world application of
multi-camera crowd counting.

考虑了使用未标记的流数据进行推理的具有异质代理和连接性的背景。代理相互合作，通过交换本地推断以及通过融合中心，使用因果框架评估代理对整体决策的实际影响。研究了反映不同代理参与模式和融合中心策略的各种场景，并推导出表示每个代理对联合决策的因果影响的表达式，以便应对异常情况，如敌对攻击或系统故障。通过数值模拟和多摄像机人群计数的实际应用验证了理论结果。

联邦边缘推理中的因果影响

Causal Influence in Federated Edge Inference

Consumer credit services offered by e-commerce platforms provide customers
with convenient loan access during shopping and have the potential to stimulate
sales. To understand the causal impact of credit lines on spending, previous
studies have employed causal estimators, based on direct regression (DR),
inverse propensity weighting (IPW), and double machine learning (DML) to
estimate the treatment effect. However, these estimators do not consider the
notion that an individual's spending can be understood and represented as a
distribution, which captures the range and pattern of amounts spent across
different orders. By disregarding the outcome as a distribution, valuable
insights embedded within the outcome distribution might be overlooked. This
paper develops a distribution-valued estimator framework that extends existing
real-valued DR-, IPW-, and DML-based estimators to distribution-valued
estimators within Rubin's causal framework. We establish their consistency and
apply them to a real dataset from a large e-commerce platform. Our findings
reveal that credit lines positively influence spending across all quantiles;
however, as credit lines increase, consumers allocate more to luxuries (higher
quantiles) than necessities (lower quantiles).

电子商务平台提供给消费者的消费信贷服务为购物提供了便利的贷款渠道，并有潜力刺激销售。本研究发展了一种基于分布估计的框架，将现有的实值回归、倒数权重和双机器学习估计器扩展为在 Rubin 的因果框架内的分布估计器，并通过一个真实数据集应用了这些估计器。我们的研究发现，信贷额度正向影响所有分位数的支出，然而随着信贷额度的增加，消费者将更多资金分配给奢侈品（高分位数）而不是必需品（低分位数）。

信用额度对消费分布的因果影响

The Causal Impact of Credit Lines on Spending Distributions

An important problem in econometrics and marketing is to infer the causal
impact that a designed market intervention has exerted on an outcome metric
over time. This paper proposes to infer causal impact on the basis of a
diffusion-regression state-space model that predicts the counterfactual market
response in a synthetic control that would have occurred had no intervention
taken place. In contrast to classical difference-in-differences schemes,
state-space models make it possible to (i) infer the temporal evolution of
attributable impact, (ii) incorporate empirical priors on the parameters in a
fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of
variation, including local trends, seasonality and the time-varying influence
of contemporaneous covariates. Using a Markov chain Monte Carlo algorithm for
posterior inference, we illustrate the statistical properties of our approach
on simulated data. We then demonstrate its practical utility by estimating the
causal effect of an online advertising campaign on search-related site visits.
We discuss the strengths and limitations of state-space models in enabling
causal attribution in those settings where a randomised experiment is
unavailable. The CausalImpact R package provides an implementation of our
approach.

本文提出了一种基于扩散回归状态空间模型的方法，用于推断经济学干预措施对于市场结果指标的因果影响，相较于差异 - 差异方案，该模型有助于灵活适应多种变异来源，并在模拟和实际数据上进行了验证。