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利用异方差神经网络学习双向因果交互AI 解读

Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence. This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments. Identification exploits conditional covariance diagonalization: when structural shocks have zero…

异质性同伴效应的识别AI 解读

We develop a model of peer effects where each peer has a separate effect depending on their rank in the distribution of peers' outcomes. Our model admits a unique equilibrium, and model parameters can be identified using peers' exogenous characteristics. To obtain a more parsimonious model of peer effects, we introduce a tractable specification based on quantile-dependent peer effect coefficients, and develop a specification test. Applying the model to several student…

高维变点检测的椭圆正则化Hotelling检验AI 解读

We propose an elliptical regularized Hotelling (ERHT) procedure for detecting location changes in high-dimensional sequences with heavy-tailed, cross-sectionally dependent observations. ERHT contrasts spatial medians on adjacent segments using a ridge-regularized inverse of the pooled centered spatial-sign covariance matrix, thereby combining robustness to radial variation with dependence-aware weighting. We establish Gaussian-process limits for the single- and…

复杂系统的推断极限：随机模型何时不可区分AI 解读

Robust inference for stochastic dynamical systems is often hampered by sparse sampling and the absence of closed-form likelihoods. We introduce a Monte Carlo path-inference framework that leverages full-path statistics and bridge processes to deliver reliable parameter estimation and model selection from coarsely sampled time series, without requiring analytical solutions. Crucially, we couple mechanistic stochastic models with their inference procedures to quantify how…

连续暴露变量分类化导致模型误设时的无模拟外推AI 解读

Epidemiological studies often categorize continuous exposures for interpretation even when the underlying outcome-exposure association is continuous. The fitted categorical regression is a misspecified model because it replaces the continuous exposure with categories. With measurement error, categorization also misclassifies latent exposure categories, so the observed regression generally targets different means and contrasts. Existing estimating-equation and…

因果推断的干预得分几何AI 解读

Let $p(x)$ be the joint density of variables $X$, and let $ψ(x)=\nabla_x\log p(x)$ be its score field. Geometry constructed from $p$ and $ψ$ alone cannot identify causal direction: structural models with the same observational distribution have the same score geometry. I develop an interventional analogue. A hard intervention $\operatorname{do}(X_k=ξ)$ does not merely reweight the joint law; it restricts the distribution to the submanifold ${x_k=ξ}$. Its score should…

增强因果全向网络重建：RECONAI 解读

Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems. Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations. We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECON), that leverages an integral-based additive nonparametric ODE model to reconstruct regulatory networks from…

辅助矩约束下的部分识别AI 解读

Partial identification is often set aside in practice because the identification regions it delivers are too wide to be useful, pushing researchers toward strong assumptions that buy point identification at the cost of credibility. We show that a source of information already sitting in most interval-valued datasets can fix this without adding any assumption at all. When an outcome is reported only as an interval---because a data custodian bracketed, top-coded, or formally…

用于协变量平衡评估的加权 KS、CVM 与 AD 检验AI 解读

Assessing covariate balance is a core diagnostic step in causal inference, but commonly used summary measures can miss meaningful distributional differences they are not designed to detect. Distributional goodness-of-fit tests, including the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Cramer-von Mises (CVM) tests, offer a more complete comparison but have previously been available only for unweighted data. We extend all three to accommodate case weights of any…

通过到高斯分布的 Wasserstein 距离进行无对比函数 ICA 与因果推断AI 解读

We study the squared $2$-Wasserstein distance to the standard Gaussian as a non-Gaussianity criterion and use it for linear Independent Component Analysis (ICA) and causal discovery in Linear Non-Gaussian Acyclic Models (LiNGAM). Unlike commonly used parametric contrasts and approximations of information-theoretic quantities, this criterion requires no distributional regularity beyond finite second moments, involves neither approximation nor tuning parameters, and can be…

从不规则且含误差的动物追踪数据推断资源选择与利用分布AI 解读

Habitat selection and space use are fundamental to understanding animal distribution. Traditional methods for quantifying habitat preferences from telemetry data assume regular sampling and negligible measurement error. However, these assumptions are routinely violated in marine systems. Practitioners typically regularize and filter the data before fitting models, but these two-step procedures do not propagate uncertainty from the filtering stage and can yield biased…

高维高斯线性回归中正则化路径变量选择的综合指南AI 解读

This paper provides a comprehensive comparison of complete regularization-path-based variable selection procedures in high-dimensional Gaussian linear regression. Our simulation study stands out from previous ones for several reasons. First, it offers comprehensive guidelines for complete variable selection, from regularization path construction to final variable subset selection. In particular, we compare jointly two convex regularization functions (Lasso and Elastic-Net)…

用于移动健康部署的扩散模型亚群体数字孪生：HeartSteps 案例AI 解读

Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants. New algorithm design decisions should therefore be vetted against realistic simulated users before each real-life deployment. We propose a method to develop ``JITAI-Twins'': digital twins of a target subpopulation for comparing candidate online…

利用处理前趋势进行双重差分推断AI 解读

Difference-in-differences (DID) are sometimes estimated with many pre-treatment periods. In such settings, the observed pre-treatment outcome evolutions provide direct information about the magnitude of shocks that could also occur after treatment. This paper proposes a simple inference procedure that uses those pre-trends as the reference distribution for the post-treatment DID. The procedure is closely related to existing conformal inference procedures, but its…

高维函数型时间序列的白噪声检验AI 解读

White noise testing is a fundamental problem in time series analysis. Yet it remains largely unsolved for high-dimensional functional time series, despite the growing attention this area has received in recent years, as existing tests are confined to either univariate functional time series or high-dimensional scalar time series. In this paper, we develop a general error-contamination framework for testing white noise in high-dimensional functional time series. We propose a…

GAUGER：干扰下基于图加权暴露水平残差化的回归调整AI 解读

Estimating causal effects under interference is a common problem in social science and economics. However, it is challenging due to the complex dependency structure induced by network connections. In this paper, we propose GAUGER, a Generalized regression Adjustment framework via Graph-weighted Exposure-level Residualization for design-based causal inference under general interference. We first reveal a surprising mismatch between accuracy and efficiency in this setting:…

时间依赖数据的高维变点分析AI 解读

This paper develops adaptive procedures for detecting and locating mean changes in high-dimensional time series. Quadratic CUSUM statistics target dense changes, whereas coordinatewise maximum statistics target sparse changes. Two weighting schemes are considered to accommodate both interior and boundary changes. Under general non-Gaussian vector dependence, we establish the limiting distributions, validate the required centering and scaling estimators, and prove asymptotic…

重尾预测变量的球协差筛选AI 解读

High-dimensional screening is commonly built on covariance, correlation, or least-squares measures. These summary measures can be unstable or even undefined, when predictors are sparse or have heavy-tailed distributions. Building on our recent work on extended codifference and the idea of Ball-covariance, we develop Ball-codifference for marginal screening in statistical modeling with heavy-tailed predictors and responses. The proposed statistic combines the rank-type…

均值线性模型中朋友的朋友工具变量的实证挑战AI 解读

In the linear-in-means model, endogeneity arises naturally due to the reflection problem. A common solution is to use Instrumental Variables (IVs) based on higher-order network links, such as using friends-of-friends' characteristics. In this paper, we show that such instruments are unlikely to work well in many applied settings due to a specific sparse/dense-network mechanism: in extremely sparse networks, friends-of-friends instruments may become degenerate, while in…

溢出效应实验中的稳健信号最大化AI 解读

We study the optimal design and analysis of experiments for estimating spillover effects. Assuming a known (e.g., linear) exposure mapping, we characterize the treatment-assignment distribution and regression-based estimator that minimize worst-case asymptotic variance against a broad class of distributions of unobservables. The design problem yields an intuitive solution in which the planner trades off spillover signal strength against diffusion of spillover variation. The…

因果效应可识别性的粒度AI 解读

The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the identifiability of state-based causal effects: how an intervention on a particular state of treatment variables affects a particular state of outcome variables. We demonstrate that state-based causal effects may be identifiable even when variable-based causal effects may not. Moreover, we show that this separation occurs only when…

利用网络数据改进对不可观测因素的控制AI 解读

This paper develops a method to conduct causal inference in the presence of unobserved confounders by leveraging networks with homophily, a frequently observed tendency to form edges with similar nodes. I introduce a concept of asymptotic homophily, according to which individuals' selectivity scales with the size of the potential connection pool. The resulting network formation model accommodates common empirical features such as homophily, degree heterogeneity, sparsity,…

CEDAR：自回归过程中的因果边发现AI 解读

We propose CEDAR (Causal Edge Discovery for Autoregressive Processes), a constraint-based method for lagged causal edge discovery in sparse autoregressive time series. CEDAR screens candidate cross-variable lags using AR(1)-residualized, U-centered distance correlation, then applies two targeted conditional-independence tests per significant cross-variable lag candidate and accepts at most one lag per ordered pair. A stable MCI pruning step removes indirect edges, and…

FARS：R 中的因子增广回归情景分析AI 解读

In the context of macroeconomic/financial time series, the FARS package provides a comprehensive framework in R for the construction of conditional densities of the variable of interest based on the factor-augmented quantile regressions (FA-QRs) methodology, with the factors extracted from multi-level dynamic factor models (ML-DFMs) with potential overlapping group-specific factors. Furthermore, the package also allows the construction of measures of risk as well as modeling…