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高维 U 统计量中显式依赖阶数的线性化AI 解读

We give an order-explicit large deviation bound for the difference between a high-dimensional $U$-statistic and its Hájek projection. In particular, we show that any $U$-statistic of order $b$ on $n$ observations, with a $d$-dimensional kernel whose coordinates have $ψ_1$-Orlicz norm at most $φ$, has a maximum deviation from its Hájek projection of order $O_p(φb n^{-1}\log^2(dn))$. The proof relies on the development of novel order-explicit moment inequalities for…

从图到动态：在不确定性下探索因果回路图的干预点AI 解读

Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems. However, as qualitative and static representations, CLDs are limited in their ability to support dynamic analysis and inform intervention strategies. We propose Diagrams-to-Dynamics (D2D), a method for converting CLDs into exploratory system dynamics models in the absence of empirical data. With minimal user…

混合总体中的因果发现AI 解读

Causal discovery aims to learn causal structures up to certain symmetries. Diverse populations or changing environments give rise to heterogeneous data in the following sense: each population/environment is a ``source'' which idiosyncratically determines the forms of causal effects. From this perspective, the source is a latent common cause for every observed variable. While some methods for causal discovery can work around latent confounding in special cases, a global…

贝叶斯因子模型教程AI 解读

Bayesian Factor Models (BFM) are well-established models that decompose the observed variability in a set of mean-zero, independent, and uncorrelated factors (random effects). While Factor Analysis (FA) was introduced in 1904 by Spearman, there has been renewed interest in inferential and computational methods that can adapt to large and complex modern data sets that are now routinely collected in a variety of applications. We provide reproducible, harmonized, and fast…

马尔可夫交替实验中经校准的期限加权局部投影设计AI 解读

We study temporal assignment design for Markov switchback experiments when the reported object is a dynamic local-projection target. We develop a calibrated selector that chooses the feasible persistence minimizing the covariance, HAC, residual-bootstrap, or realized-schedule risk of the estimator and reporting object specified before the experiment. A balanced homoskedastic Markov benchmark yields a closed form because the lagged-assignment information matrix is…

存在未知混杂因素时的二元关系处理效应估计AI 解读

This paper proposes estimation and inference methods for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a…

非参数时间序列回归中跳跃的高维推断AI 解读

We study simultaneous inference on jumps in the conditional mean functions of a high-dimensional collection of heterogeneous nonparametric time series, where the number of series may exceed the sample size and the data may exhibit strong cross-sectional dependence. The jump depends on one specific covariate, and we allow the regression function to vary with additional latent variables. We propose two uniform tests: one for the existence of jumps and one for their homogeneity…

无分位数交叉的分位数向量自回归AI 解读

This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, violating the monotonicity of quantiles. To address this issue, we propose a simplex quantile vector autoregression (SQVAR) framework, which transforms the autoregressive (AR) structure of the original QVAR model into a simplex, ensuring that the estimated quantile curves remain monotonic across all…

disco：分布合成控制AI 解读

The method of synthetic controls is widely used for evaluating causal effects of policy changes in settings with observational data. Often, researchers aim to estimate the causal impact of policy interventions on a treated unit at an aggregate level while also possessing data at a finer granularity. In this article, we introduce the new disco command, which implements the Distributional Synthetic Controls method introduced in Gunsilius (2023, Econometrica 91: 1105-1117).…

具有飞轮效应的分享诱导参与度因果估计AI 解读

Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-designed sharing feature can trigger a self-reinforcing ``flywheel effect'': reactivated users become potential sharers whose engagement propagates through the network over multiple rounds, amplifying total engagement. Measuring the causal impact of such sharing features is challenging, as their…

规则网格上空间随机场均值变化的检测AI 解读

We propose statistical procedures for detecting changes in the mean of spatial random fields observed on regular grids. The proposed framework provides a general approach to change detection in spatial processes. Extending a block-based method originally developed for time series, we introduce two test statistics, one based on Gini's mean difference and a novel variance-based variant. Under mild moment conditions, we derive asymptotic normality of the variance-based…

隐私约束下的 Knockoff 推断AI 解读

Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. In this paper, we propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy…

筛选何时误导：用于可靠因果发现的稳健孟德尔随机化检验AI 解读

Mendelian Randomization (MR) has been widely used as a standard approach for identifying causal effects in biomedical research. When the true exposure and outcome variables are unknown among many potential candidates, a common but problematic practice is to first screen for associations and then evaluate causal effects only among exposure-outcome pairs that show significant correlations. We demonstrate that the classical MR ratio estimator suffers from severe type I error…

面向高维数据的快速可扩展逐单元稳健集成方法AI 解读

Variable selection and ensemble methods are central to high-dimensional modelling, enabling the identification of relevant predictors and the construction of stable predictive signals through aggregation across multiple models. However, in practice, high-dimensional data are often affected by cellwise contamination, in which individual cells of the data matrix deviate from the underlying multivariate structure without necessarily making the corresponding observation…

中介存在未观测混杂的前门因果结构：近端识别与估计AI 解读

Unobserved confounding is a fundamental obstacle in causal inference problems. In the graphical modeling literature, a general theory has been developed that allows identification in the presence of hidden variables, with some limitations. In particular, Pearl's celebrated front-door criterion allows nonparametric identification in the presence of unobserved common causes of the treatment and the outcome, however it requires the presence of an unconfounded variable that…

采用局部—全局收缩的非正态变换有向无环图半参数贝叶斯结构学习AI 解读

Bayesian structure learning of directed acyclic graphs (DAGs) is central to high-dimensional causal discovery, yet existing methods mostly assume multivariate Gaussian data, which is routinely violated by measurements displaying heavy tails, skewness, or bounded support. We address this by introducing NPN-DAG-HS, a fully Bayesian semiparametric DAG model coupling the nonparanormal family with horseshoe shrinkage on Cholesky off-diagonals via an extended-rank likelihood. This…

通过 Riesz 表示构建现代因果面板估计量的贝叶斯稳健性值AI 解读

We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial…

通过拉普拉斯近似实现可扩展的贝叶斯有向无环图结构学习及乳腺癌应用AI 解读

Structure learning of directed acyclic graphs (DAGs) from observational data is a foundational task in causal discovery and is widely used to infer regulatory networks from medical and genomic measurements. The Bayesian formulation quantifies model uncertainty and admits prior biological knowledge, but its practical use has been hampered by the super-exponential growth of the DAG space and by the intractability of the node-marginal likelihood under flexible, non-conjugate…

高效组套索正则化秩回归与基于模拟的调参AI 解读

High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods. To improve robustness, we adopt a non-smooth Wilcoxon score based rank objective and incorporate the group sparsity regularization. By extending the tuning-free property originally developed for the rank Lasso, we introduce a simulation-based tuning rule and further establish a finite-sample error bound for the resulting…

快速偏分位数回归AI 解读

Partial least squares (PLS) is a dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in situations where the data is colinear or high dimensional. Both PLS and OLS provide mean based estimates, which are extremely sensitive to the presence of outliers or heavy tailed distributions. In contrast, quantile regression is an alternative to OLS that computes robust quantile based estimates. In this work, the multivariate PLS is extended to the…

有向无环图上的深度高斯过程AI 解读

Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-regulatory networks, to transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and…

媒体效果衡量与“助攻乌龙”：归因、营销组合模型及个体增量效应AI 解读

We use the assisted own goal hypothesis as a lens into media measurement. A demand-generating (upper-funnel) advertising platform such as a short-video social network can cause an incremental purchase, yet see that purchase booked on -- and credited to -- a downstream trusted marketplace, because consumers who discover a product on the platform complete the transaction elsewhere, for example because of distrust of the generating platform as a psychological mechanism. Under…

混合解释变量下的误设回归：稳健推断与因果解释AI 解读

For analytic convenience, existing statistical frameworks either assume random or fixed regressors. However, it is a little awkward that they do not cover the practical case of estimating the average treatment effect in experiments with randomized treatments and non-randomized, fixed pretreatment covariates. We unify the literature by providing the theory for regressions with mixed regressors that contain both random and fixed components. Importantly, our theory allows for…

斯大林是否必要？苏联经济增长的反事实证据AI 解读

This article evaluates whether the exceptionally rapid growth of the Russian and Soviet economy after 1928 requires a specifically Stalinist explanation. The analysis treats this historical question as a sequence of increasingly demanding counterfactual comparisons rather than a binary comparison between observed growth and a single alternative. Long run output series from the Global Macro Database and the Maddison Project are combined with synthetic control, augmented…