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多区域临床试验中区域处理效应的有效估计AI 解读

A multi-regional clinical trial (MRCT) is a single clinical trial conducted in multiple regions simultaneously under a common protocol, which may be used to support parallel submissions to multiple regulatory authorities. For a regional regulatory authority, treatment effects defined specifically for its own region are more relevant to consider than overall treatment effects based on all regions included in an MRCT. A regional treatment effect can be estimated consistently…

中介分析的能量平衡权重AI 解读

Causal mediation analysis requires reconstruction of counterfactual distributions to estimate natural direct and indirect effects. Inverse probability weighting estimators rely on models for treatment assignment and mediator density ratios, whereas moment balancing approaches require researchers to specify in advance which functions of the covariates and mediators should be balanced. We propose Energy Balancing Weights for Mediation Analysis (EBWMA), which targets the joint…

存在处理不依从与多个中介时零膨胀纵向数据的因果中介分析AI 解读

Understanding whether a digital marketing campaign is effective is central to designing effective customer engagement strategies. We analyze a large-scale, longitudinal promotional email campaign conducted by a U.S.\ retailer to evaluate how value-added incentives, such as free shipping, compare with traditional price discounts in influencing customer purchasing behavior. The analysis is complicated by non-compliance, due to not opening emails, multiple longitudinal…

确定因果方向需要多少样本？双变量 LiNGAM 的尖锐极小极大界AI 解读

We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quantify the difficulty when the causal effect is weak or the disturbances are nearly Gaussian. Let $β$ bound the absolute structural coefficient from below, let $ν$ measure each standardized disturbance's distance from Gaussianity, and let the…

个体与整群随机试验中存活期间目标量的双重稳健估计AI 解读

Randomized trials in chronic disease settings often measure treatment benefit through recurrent non-fatal events that are truncated by death, where conventional summaries either discard recurrences, conflate the treatment effect with survival, or treat death as censoring and forfeit a causal interpretation. While-alive estimands measure event burden per unit time alive, but doubly robust estimation for the exposure-weighted while-alive rate remains undeveloped, particularly…

调查抽样与因果推断中的最优控制变量AI 解读

We propose a family of control variate estimators for variance reduction in design-based survey sampling and causal inference, with and without interference. In these settings, inverse probability weighting (IPW) estimators are widely used, but may have large variance when sampling, treatment, or exposure probabilities are small. Building on the observation that several common estimators, including the Hajek estimator, the normalized estimator, the augmented inverse…

从风险预测到风险机制：道路安全与车险的多分辨率因果表示AI 解读

Operational risk models can estimate event frequencies precisely while leaving the underlying mechanisms weakly resolved. We study this resolution mismatch using motor insurance and road safety. A revisable DAG represents trip-level crash generation; a separate predictive layer links annual rating information to latent driving states; and an observation process maps crashes into recorded liability claims. Compatibility sets collect the structural and crash-to-claim laws that…

协变量数量发散时协变量自适应随机化的理论性质AI 解读

Covariate-adaptive randomization procedures are widely used in clinical trials to improve covariate balance. In modern applications, experimenters often have access to many covariates, motivating the need for a theory of covariate-adaptive randomization procedures with a diverging number of covariates. This paper studies two classes of covariate-adaptive randomization procedures, referred to as imbalance-efficient covariate-adaptive randomization and imbalance-robust…

有限重叠下平均处理效应的估计：多项式逼近与外推AI 解读

Estimating the average treatment effect (ATE) remains a fundamental challenge in observational studies in the presence of poor or limited covariate overlap. Although the inverse probability weighting (IPW) estimator is a widely used approach for estimating the ATE, its performance can deteriorate substantially when overlap is limited, often resulting in increased finite sample bias and unreliable confidence intervals. One common strategy is to shift attention from the…

利用聚类信息的逆概率加权因果效应估计策略AI 解读

Inverse probability weighting (IPW) is widely used to estimate causal effects in observational studies but depends on adequate propensity-score specification. We compare three strategies for addressing treatment assignment heterogeneity: standard IPW, clustering augmented IPW with cluster specific propensity score models, and a global propensity score model including estimated cluster membership as a covariate. Through simulations with and without latent cluster structure…

潜在结果有界时的最优实验设计与估计AI 解读

I study the optimal design and analysis of randomized experiments for estimating finite-population average treatment effects when potential outcomes are known to be bounded, as with binary outcomes. Among all assignment mechanisms and a broad class of affine estimators, worst-case mean-squared error (MSE) is minimized by independent random assignment and an unconventional regression of the support-midpoint-centered outcome on the recentered treatment, with no intercept. This…

秩—秩回归的统计推断AI 解读

The slope coefficient in a rank-rank regression is a popular measure of intergenerational mobility. In this article, we first show that commonly used inference methods for this slope parameter are invalid. Second, when the underlying distribution is not continuous, the OLS estimator and its distribution may be sensitive to how ties in the ranks are handled. Motivated by these findings we derive the asymptotic distribution of the OLS estimator and provide valid inference…

甜甜圈断点回归设计AI 解读

Donut regression discontinuity (RD) designs exclude observations in a neighborhood around the cutoff. They are widely used in empirical work to address concerns about manipulation, sorting, or measurement error in the running variable. Despite their popularity, donut RD estimators are typically implemented heuristically, with limited formal guidance and unclear implications for bias, variance, and statistical inference. This paper provides a theoretical analysis of donut RD…

利用生成式人工智能对非结构化数据开展因果推断AI 解读

We introduce GenAI-Powered Inference (GPI), a statistical framework for both causal and predictive inference using unstructured data, including text and images. GPI leverages open-source Generative Artificial Intelligence (GenAI) models---such as large language models and diffusion models---not only to generate unstructured data at scale but also to extract low-dimensional representations that are guaranteed to capture their underlying structure. Applying machine learning to…

网络不确定性下的网络调整 GMM 估计AI 解读

This paper proposes a network-adjusted generalized method of moments (NA-GMM) estimator for social interaction models when the observed network may differ from the true interaction network. NA-GMM is a novel penalized GMM approach that allows the elements of the observed interaction matrix to be modified to improve the fit of the moment conditions. To avoid unrestricted network adjustments, the NA-GMM criterion introduces a penalty on the amount of adjustment. Since NA-GMM…

交叉设计的系统分析：因果效应、有效估计与稳健推断AI 解读

Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However, standard analyses of crossover designs…

整合随机试验、真实世界数据、人工智能与统计学：下一代证据综合AI 解读

Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Studies using real-world data (RWD), historically considered less reliable for establishing causality, are now recognized as an important source of real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are increasingly used throughout the drug…

诚实置信区间的局部渐近功效AI 解读

Confidence intervals that are conservative against an untestable bias, called bias-aware or honest, are now standard in DiD, IV, RD, and factor-model settings. This paper characterises the local power of the tests they induce. Power is governed by the rate of the bias bound relative to the sampling rate, giving three regimes: when the bound vanishes faster than the standard error, conservatism is asymptotically free; when the two are of the same order it costs a bounded,…

群体互动实验的统计推断AI 解读

A common experimental research design is one in which individuals are randomly allocated into groups that then interact under different group-level treatment conditions. We develop design-based inference for such "group interaction" experiments, covering scenarios in which groups are either fixed or randomly formed and in which potential outcomes are either fixed relative to others' group assignments or subject to interference. For each scenario, we characterize the causal…

三维面板与网络数据固定效应估计的去偏推断AI 解读

Inference for fixed effects estimators is often unreliable due to Nickell- and incidental parameter biases. While these issues are well understood for classical two-dimensional panels, little is known about three-dimensional panel structures (e.g., sender x receiver x time). We develop inferential theory for a broad class of linear and nonlinear fixed effects M-estimators in this setting, covering bipartite, directed, and undirected network panel data, multiple…

基于半参数累积概率模型的双重稳健分位数处理效应估计AI 解读

The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distribution can provide additional information in biomedical research. Quantile treatment effect (QTE) captures such distributional differences, particularly when outcomes are skewed. However, existing approaches for estimating QTE make distributional assumptions about the outcome and are thus sensitive to…

使用非同期对照的平台试验因果生存分析AI 解读

Platform trials allow treatment arms to enter and exit over time while maintaining a shared control arm, yielding concurrent and non-concurrent controls (NCC). Pooling NCC is often motivated as a strategy to improve statistical efficiency, but it is unclear which estimand is targeted, what assumptions justify identification and estimation, and when precision gains are achievable; these questions are further complicated by time-to-event/survival data. Motivated by the…

含交互效应的超高维因果中介分析去偏估计AI 解读

Mediation analysis is a crucial tool for uncovering the mechanisms through which a treatment affects an outcome, providing deeper causal insights and guiding effective interventions. Despite substantial advances in mediation analysis with fixed- or low-dimensional mediators and covariates, estimation and inference for mediation functionals remain limited when both mediators and covariates are ultra-high-dimensional. In this paper, we propose an estimator for the mediation…

竞争风险生存结局的亚组平均处理效应双重稳健目标估计AI 解读

In recent years, precision treatment strategies have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating subgroup average treatment effects (ATE) in time-to-event data with competing risks, using ICU patients with sepsis as an illustrative example. Our approach, based on cumulative incidence functions and targeted maximum likelihood estimation (TMLE), achieves both asymptotic efficiency and double…