每日简报

超越效用：将眼动、皮肤电导与心率纳入出行行为模型AI 解读

Choice models for large-scale applications have historically relied on economic theories (e.g. utility maximisation) that establish relationships between the choices of individuals, their characteristics, and the attributes of the alternatives. In a parallel stream, choice models in cognitive psychology have focused on modelling the decision-making process, but typically in controlled scenarios. Recent research developments have attempted to bridge the modelling paradigms,…

秘鲁矿业米塔制度的持久影响：双重机器学习方法AI 解读

This study examines the long-term economic impact of the colonial Mita system in Peru, building on Melissa Dell's foundational work on the enduring effects of forced labor institutions. The Mita, imposed by the Spanish colonial authorities from 1573 to 1812, required indigenous communities within a designated boundary to supply labor to mines, primarily near Potosi. Dell's original regression discontinuity design (RDD) analysis, leveraging the Mita boundary to estimate the…

利用外部对照提高随机试验累积发生率估计的精度AI 解读

Augmenting the control arm in clinical trials with external data can improve statistical power for demonstrating treatment effects. In many time-to-event outcome trials, participants are subject to truncation by death. Direct application of methods for competing risks analysis on the joint data may introduce bias, for example, due to covariate shifts between the populations. In this work, we consider transportability of the conditional cause-specific hazard of the event of…

利用特异性进行观察研究中的因果推断AI 解读

Hill's specificity criterion has been highly influential in biomedical and epidemiological research. However, it remains controversial and its application often relies on subjective and qualitative analysis without a comprehensive and rigorous causal theory. Focusing on unmeasured confounding adjustment with multiple treatments and multiple outcomes, this paper develops a formal and quantitative framework for leveraging specificity for causal inference in observational…

泊松有向图模型的似然比检验AI 解读

Directed acyclic graphs are widely used to describe the causal effects among random variables, and the inference of those causal effects has become an popular topic in statistics and machine learning, and has wide applications in neuroinformatics, bioinformatics and so on. However, most studies focus on the estimation or inference of the directional relations among continuous random variables, those among discrete random variables have not gained much attentions. In this…

底层有向混合图上的簇图：聚合层面因果效应识别AI 解读

The do-calculus is a sound and complete tool for identifying causal effects in acyclic directed mixed graphs (ADMGs) induced by structural causal models (SCMs). However, in many real-world applications, especially in high-dimensional setting, constructing a fully specified ADMG is often infeasible. This limitation has led to growing interest in partially specified causal representations, particularly through cluster-directed mixed graphs (C-DMGs), which group variables into…

协变量不可忽略缺失下最优动态治疗方案的稳健估计AI 解读

Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of…

用于帕金森病生物标志物发现的稀疏因果结构学习：惩罚化 FCIAI 解读

Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD…

容量约束下的最优处理分配规则AI 解读

We study treatment assignment problems under capacity constraints, where a planner aims to maximize social welfare by assigning treatments based on observable covariates. Such constraints, common when treatments are costly or limited in supply, introduce nontrivial challenges for deriving optimal statistical assignment rules because the planner needs to coordinate treatment assignment probabilities across the entire covariate distribution. To address these challenges, we…

极端分类群偏差下的生态网络连接预测AI 解读

Ecological networks offer powerful insights into community function, but without first characterizing these networks accurately, our ability to detect and interpret changes under environmental stress is limited. We develop an approach to reduce bias in link prediction in the common scenario in which data are derived from studies focused on a small number of species. Our Extended Covariate-Informed Link Prediction (COIL+) framework employs a latent factor model that flexibly…

揭示工具变量回归中谱特征学习的机制AI 解读

We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spectral features - that is, learned features spanning the top eigensubspaces of the operator linking treatments to instruments. We derive a generalization error bound for a two-stage least squares estimator based on spectral features, and gain insights into the method's performance and failure modes.…

倾向得分加权中的欠平滑LASSO与合成负对照暴露偏差检测AI 解读

The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection operator (LASSO) has become the most widely used tool for fitting large-scale PS models in these settings. LASSO uses L1 regularized regression to prevent overfitting by shrinking coefficients toward zero (setting some exactly to zero). The degree of regularization is typically selected using…

存在不依从的两阶段处理：用多地点工具变量识别累积平均效应AI 解读

When evaluating a two-phase intervention, the cumulative average treatment effect (ATE) is often the primary causal estimand of interest. However, some individuals who do not respond well to the Phase I treatment may subsequently display noncompliant behaviors. At the same time, exposure to the Phase I treatment is expected to directly influence an individual's potential outcomes, thereby violating the exclusion restriction. Building on an instrumental variable (IV) strategy…

加权受控直接效应非参数估计的最优调整集AI 解读

The weighted controlled direct effect (WCDE) generalizes the standard controlled direct effect (CDE) by averaging over the mediator distribution, providing a robust estimate when treatment effects vary across mediator levels. This makes the WCDE especially relevant in fairness analysis, where it isolates the direct effect of an exposure on an outcome, independent of mediating pathways. This work establishes three fundamental advances for WCDE in observational studies: First,…

暴露与结局均存在缺失时的因果推断AI 解读

Missing data are ubiquitous in public health research. When estimating causal effects, there are well-established methods to address bias to due missing outcomes. Commonly, causal estimands are defined under hypothetical interventions to "set" the exposure and to prevent missingness. We demonstrate how this framework can be extended to missing exposures. We further extend this framework to incorporate missingness on the baseline outcome, which induces missingness on the…

评估扩大筛查与改善结局策略总效应的因果框架AI 解读

For many health conditions, there are highly efficacious treatment and prevention products. Maximizing their impact requires strategies that improve the reach of health screening in order to establish who could benefit. For example, HIV prevention strategies aim to expand risk screening and to improve uptake of pre-exposure prophylaxis (PrEP) among those experiencing risk. Often, these strategies induce changes at the group-level (e.g., health clinics or communities) and are…

缺乏严格重叠时的分位数处理效应估计AI 解读

We consider the problem of estimating quantile treatment effects without assuming strict overlap, i.e., we do not assume that the propensity score is bounded away from zero. More specifically, we consider an inverse probability weighting (IPW) approach for estimating quantiles in the potential outcomes framework and pay special attention to scenarios where the propensity scores can tend to zero as a regularly varying function. Our approach effectively considers a…

多暴露与多结局空间回归的谱混杂调整AI 解读

Characterizing social vulnerability is fundamental to disaster response planning. Numerous vulnerability indicators have been developed, but they are typically not validated for their predictive power over the outcomes of interest. As with many environmental health studies where interventions are impractical or unethical, validation of social vulnerability against public health outcomes is observational and relies on spatially-dependent data. Observational studies are…

测地空间中函数型数据与随机对象的回归不连续设计AI 解读

Regression discontinuity designs (RDDs) are widely used for causal inference in observational studies with cutoff-based treatment assignment, primarily for Euclidean outcomes. We propose the geodesic regression discontinuity design (GRDD), which extends RDDs to complex non-Euclidean outcomes, including networks, compositional data, functional data, and other random objects in geodesic metric spaces. Since algebraic operations are unavailable in such spaces, we define the…

粗粒度个性化：联合分群与处理配置AI 解读

With advances in estimating heterogeneous treatment effects, firms can personalize and target individuals at a granular level. However, feasibility constraints limit full personalization. In practice, firms choose segments of individuals and assign a treatment to each segment to maximize profits: We call this the coarse personalization problem. We propose a two-step solution that simultaneously makes segmentation and targeting decisions. First, the firm personalizes by…

处理水平可增长且组规模不同时设计型因果推断的 Berry–Esseen 界AI 解读

Neyman (1923/1990) introduced the randomization model, which contains the notation of potential outcomes to define causal effects and a framework for large-sample inference based on the design of the experiment. However, the existing theory for this framework is far from complete, especially when the number of treatment levels diverges and the treatment group sizes vary. We provide a unified discussion of statistical inference under the randomization model with general…

网络实验中的因果推断：回归分析与设计性质AI 解读

Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the…

中等高维协变量下完全随机实验的去偏回归调整AI 解读

Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the…

序贯合成双重差分AI 解读

We propose the Sequential Synthetic Difference-in-Differences (Sequential SDiD) estimator for event studies with staggered treatment adoption, particularly when the parallel trends assumption fails. The method uses an iterative imputation procedure on aggregated data, where estimates for early-adopting cohorts are used to construct counterfactuals for later ones. We prove the estimator is asymptotically equivalent to an infeasible oracle OLS estimator within a linear model…