每日简报

多个误分类暴露的因果推断：控制变量调整的校准加权方法AI 解读

Exposure misclassification is a common issue prevalent in studies of respiratory infections in cystic fibrosis. Throat swabs are frequently used in place of expectorated or induced sputum cultures, although they have imperfect sensitivity and specificity to detect Pseudomonas aeruginosa and Staphylococcus aureus. We develop calibration weighting and control variate estimators for causal inference with multiple misclassified binary exposures and clustered observations. The…

无穷小因果性AI 解读

Interventions can be varied continuously in many causal models. Differentiating a specified smooth intervention protocol produces vector fields on a statistical model, and their Lie brackets describe the noncommutativity of the corresponding local perturbations. We formulate this differential geometry of interventions on smooth statistical models and call the resulting framework infinitesimal causality (IC). Given a constant-rank distribution spanned by visible intervention…

线性结构方程模型半 trek 估计量的半参数推断AI 解读

Linear structural equation models on directed mixed graphs encode causal relationships among variables subject to latent confounding. The half-trek criterion (HTC) provides a graphical sufficient condition for the structural coefficients to be rationally identifiable from the observable covariance matrix, and yields a corresponding closed-form rational estimator. Despite this, the asymptotic distribution of the HTC estimator, and hence valid standard errors and confidence…

具有潜在组结构的矩阵值网络自回归模型AI 解读

Matrix-valued time series data are frequently observed in a broad range of areas and have attracted great attention recently. In this work, we model network effects for high dimensional matrix-valued time series data in a matrix autoregression framework. To characterize the potential heterogeneity of the subjects and handle the high dimensionality simultaneously, we assume that each subject has a latent group label, which enables us to cluster the subject into the…

双边市场双重随机化实验的回归调整AI 解读

Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for estimating total, spillover, and direct effects in MRDs. We derive minimum asymptotic variance estimators among a broad class of linearly adjusted estimators, without assuming a linear model on the potential outcomes. Surprisingly, the optimal regression adjustments are estimable from data and are…

线性结构方程模型中偏回归系数因果解释的统一图判据AI 解读

This paper provides a graph-based characterization of partial regression coefficients in linear structural equation models. First, we derive a generalized graphical criterion that unifies the d-separation, single-door, and back-door criteria. This criterion provides a generically necessary and sufficient condition under which a partial regression coefficient coincides with a linear causal effect that is not mediated by other explanatory variables. Second, we clarify the…

真实处理不可观测时的因果推断AI 解读

In many causal inference settings, the treatment of interest is not directly observed; instead, one or more error-prone proxy measurements are available, creating a fundamental identification challenge. Building on identification methods for hidden treatments with proxies, we develop a general semiparametric framework for causal effect estimation that accommodates several common estimands. Hidden treatment models differ fundamentally from classical missing-data settings,…

州级政策异质性分析：问题辨析与方法建议AI 解读

State-level policy studies often conduct heterogeneity analyses that quantify how treatment effects vary across state characteristics. These analyses may be used to inform state-specific policy decisions, or to infer how the effect of a policy changes in combination with other state characteristics. However, in state-level settings with varied contexts and policy landscapes, multiple versions of similar policies, and differential policy implementation, the causal quantities…

基于张量的二阶因果发现AI 解读

Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of observational and interventional data. Assuming the causal dependencies follow a linear structural equation model on a directed acyclic graph (DAG), TSCD outputs the DAG and the functions on its edges, requiring only that the noise variables…

随机对照试验中 AIPW 估计器 Wald 区间覆盖率的模型无关界AI 解读

Nonparametric estimators, such as the augmented inverse probability weighted (AIPW) estimator, have become increasingly popular in causal inference. Numerous nonparametric estimators have been proposed, but they are all asymptotically normal with the same asymptotic variance under similar conditions, leaving little guidance for practitioners to choose an estimator. In this paper, I focus on another important perspective of their asymptotic behaviors beyond asymptotic…

放宽可迁移性假设的随机试验效应外推：美国选举政治广告案例AI 解读

During the 2020 U.S. presidential election, Aggarwal et al. (2023) conducted a large-scale randomized experiment to evaluate a digital ad campaign against Trump in five battleground states. While the study found no effect on voter turnout, it's unclear whether this null result generalizes to other battleground states, notably Georgia, which played a unique role in the 2020 election and differs from the battleground states. Inspired by the study, we present a transfer…

一般处理模型中的高阶去偏估计器AI 解读

It is now well known that estimators based on influence functions can be sub-optimal in terms of convergence rates in various settings. To address this issue, higher-order influence functions (HOIF) are developed, generalizing the classical semiparametric theory. However, most existing results in this regard focus on treatment effect parameters defined in explicit forms, such as average treatment effects (ATE). In applications, economists are often confronted with tasks of…

处理变化何时能够识别因果效应？AI 解读

This paper clarifies the identifying assumptions underlying causal inference based on treatment changes rather than levels, and their relationship to conventional identification strategies. We characterize two structural models, with non-nested assumptions, under which treatment-change identification is valid conditional on observed covariates by differencing out time-constant confounders that are additive in the treatment equation. The assumptions underlying treatment…

基于大语言模型的 A/B 测试：人类因果推断的替代终点框架AI 解读

Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We study when a treatment effect estimated on LLM outcomes can recover the effect for the human population of interest. Distributional equivalence between LLM and human outcomes would make any standard estimator valid but is unrealistic. We therefore develop a statistical framework…

传统分解与因果分解何时分歧？健康差异研究的实用指南AI 解读

Identifying malleable factors that can reduce social disparities is a central objective among researchers across disciplines. Traditionally, researchers have relied on the difference-in-coefficients and Kitagawa-Oaxaca-Blinder frameworks. More recently, methods grounded in the potential outcomes framework have emerged. While these methods share the same goal of identifying drivers of disparity, they frequently yield divergent results depending on the underlying confounding…

网络化风险感知与行为泡沫：疫情案例AI 解读

Risk perception is typically modeled as an individual cognitive readout of objective hazard, yet during crises what people judge as risky is shaped by what their peers do. Using weekly mobility data from 313 Massachusetts municipalities over the first year of the COVID-19 pandemic and a pre-pandemic inter-town mobility network that fixes interaction structure before the shock, we estimate two-way fixed-effects panel regressions that separate local case response, inter-town…

正交化合成控制AI 解读

When conducting inference for the average treatment effect on the treated with a Synthetic Control Estimator, the vector of control weights is a nuisance parameter that is often constrained, high-dimensional, and may be only partially identified even when the average treatment effect on the treated is point-identified. All three of these features of a nuisance parameter can lead to failure of asymptotic normality for the estimate of the parameter of interest when using…

Napkin 因果图下的因果推断AI 解读

Unmeasured confounding can render identification strategies based on adjustment functionals invalid. We study the "Napkin" graph, a causal structure that encapsulates features of M-bias, instrumental variables, and classical back-door and front-door settings, yet identifies the average treatment effect through a nonstandard ratio of two g-formulas. We develop influence-function-based estimators for this functional, including doubly-robust one-step and targeted minimum…

序贯决策中因果效应的置信区间AI 解读

We derive confidence intervals and confidence sequences for causal effects in situations where the back-door criterion is applicable. Our tightest confidence intervals hold in the standard setting where the training data consists of IID observations over a system described by a given causal diagram. When interventions are allowed to depend on the past data, our confidence intervals become wider and involve a term coming from the law of the iterated logarithm, even where the…

最优策略可能不唯一时的半参数离策略价值推断AI 解读

Off-policy evaluation (OPE) constructs confidence intervals for the value of a target policy using data generated under a different behavior policy. Most existing inference methods focus on fixed target policies and may fail when the target policy is estimated as optimal, particularly when the optimal policy is non-unique or nearly deterministic. We study inference for the value of optimal policies in Markov decision processes. In an auxiliary augmented transition-sampling…

利用网络结构的工具变量回归：因果节点发现与估计AI 解读

Estimating causal effects from high-dimensional, structured exposures is a fundamental challenge in modern applications ranging from neuroscience and finance to environmental science. While the literature has addressed high-dimensional instrumental variable (IV) regression, and separately leveraged graph structure in penalized regression, the integration of both, especially for causal support recovery in the presence of latent confounding, remains unexplored. In this work,…

非可分选择模型估计：函数压缩映射方法AI 解读

We propose a novel method for estimating nonseparable selection models. We show that, for a given selection function and under suitable contraction conditions, the potential outcome distributions are nonparametrically identified from the selected outcome distributions and can be recovered using a simple iterative algorithm. This result enables a full-information approach to estimating selection models without imposing parametric or separability assumptions on the outcome…

存在单位间干扰时的低秩协变量平衡估计AI 解读

A key methodological challenge in observational studies with interference between units is twofold: (1) each unit's outcome may depend on many others' treatments, and (2) treatment assignments may exhibit complex dependencies across units. We develop a general statistical framework for constructing robust causal effect estimators to address these challenges. We first show that, without restricting the patterns of interference, the standard inverse probability weighting (IPW)…

非参数分割型 M 估计的一致估计与统一推断AI 解读

This paper presents uniform estimation and inference theory for a large class of nonparametric partitioning-based M-estimators. The main theoretical results include: (i) uniform consistency for convex and non-convex objective functions; (ii) rate-optimal uniform Bahadur representations; (iii) rate-optimal uniform (and mean square) convergence rates; (iv) valid strong approximations and feasible uniform inference methods; and (v) extensions to functional transformations of…