每日简报

Itô 过程的条件局部独立检验及动态因果发现应用AI 解读

Inferring causal relationships from dynamical systems is the central interest of many scientific inquiries. Conditional local independence, which describes whether the evolution of one process is influenced by another process given additional processes, is important for causal learning in such systems. In this paper, we propose a hypothesis test for conditional local independence in Itô processes. Our test is grounded in the semimartingale decomposition of the Itô process,…

汇总因果图中的平均受控与平均自然微观直接效应AI 解读

In this paper, we investigate the identifiability of average controlled direct effects and average natural direct effects in causal systems represented by summary causal graphs, which are abstractions of full causal graphs, often used in dynamic systems where cycles and omitted temporal information complicate causal inference. Unlike in the traditional linear setting, where direct effects are typically easier to identify and estimate, non-parametric direct effects, which are…

PrivATE：平均处理效应的差分隐私置信区间AI 解读

The average treatment effect (ATE) is widely used to evaluate the effectiveness of drugs and other medical interventions. In safety-critical applications like medicine, reliable inferences about the ATE typically require valid uncertainty quantification, such as through confidence intervals (CIs). However, estimating treatment effects in these settings often involves sensitive data that must be kept private. In this work, we present PrivATE, a novel machine learning…

不可对角化矩阵不变子空间的假设检验及网络统计应用AI 解读

We generalise the inference procedure for eigenvectors of symmetrizable matrices of Tyler (1981) to that of invariant and singular subspaces of non-diagonalizable matrices. Wald tests for invariant vectors and $t$-tests for their individual coefficients perform well in simulations, despite the matrix being not symmetric. Using these results, it is now possible to perform inference on network statistics that depend on eigenvectors of non-symmetric adjacency matrices as they…

空间与时间处理效应边界的统一框架：理论与识别AI 解读

This paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both spatial and temporal dimensions. We formalize the concept of treatment effect boundaries as structural parameters characterizing regime transitions where causal effects cease to operate. Building on reaction-diffusion models of information propagation, we establish conditions under which spatial and temporal boundaries share common dynamics governed by…

链接部分可观测的团队网络AI 解读

This paper studies a linear production model in team networks with missing links. In the model, heterogeneous workers, represented as nodes, produce jointly and repeatedly within teams, represented as links. Links are omitted when their associated outcome variables fall below a threshold, resulting in partial observability of the network. To address this, I propose a Generalized Method of Moments estimator under normally distributed errors and develop a distribution-free…

多阶段分层抽样与学生未参与下国际大型测评平均成绩的部分识别AI 解读

International large-scale assessment (ILSA) studies collect information across education systems with the objective of learning about the population-wide distribution of student achievement in the assessment. In this article, we study one of the most fundamental threats that these studies face when justifying the conclusions reached about these distributions: the identification problem that arises from student non-participation during data collection. Recognizing that ILSA…