论文检索

样本与类别扰动下多类别数据的均值比估计AI 解读

Summay We consider the problem of estimating ratios of means of a multivariate outcome across covariates when the data are observed with unknown sample-specific and category-specific perturbations. Our model admits a partially identifiable estimand, and we establish full identifiability by imposing interpretable parameter constraints. To reduce bias and guarantee the existence of estimators in the presence of sparse observations, we apply an asymptotically negligible and…

利用代理控制结局的数据整合后推断AI 解读

Summary Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variation, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by leveraging control outcomes. Using causal interpretations, we…

匹配估计量的自助法一致性AI 解读

Summary In a landmark paper, Abadie & Imbens (2008)showed that the naive bootstrap is inconsistent when applied to nearest neighbour matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In contrast, in this work we show that the naive bootstrap is provably consistent for the original matching…

存在不依从时二水平多因素实验的因果效应界AI 解读

Summary Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned factor, identification and estimation of the average treatment effects become impossible without strong assumptions. Leveraging an instrumental variables approach, previous studies have shown how to define and estimate the causal effect of treatment uptake among respondents who comply with treatment. A major caveat is that these results rely on…

非参数加性回归中交互效应的识别与估计AI 解读

Abstract A new formulation of the additive interaction model is introduced. In contrast to existing approaches, the new formulation separates well the joint effects of covariates that cannot be accounted for by individual main effects. The new approach enables correct interpretation of interaction effects by making them orthogonal to the associated main effects in the $ L^{2} $ sense. A new method is developed to estimate the resulting main and interaction effects.…

近端间接治疗比较AI 解读

Summary We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial, but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We…

局部因果发现中的结构限制：识别目标变量的直接原因AI 解读

Summary We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one…

空间自回归模型的稳健估计AI 解读

This article proposes and studies two Huber-type estimation approaches, namely, the Huber instrumental variable (IV) estimation and the Huber generalized method of moments (GMM) estimation, for a spatial autoregressive model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influence functions of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares…

带动态协变量的统计排序AI 解读

Abstract We introduce a general covariate-assisted statistical ranking model within the Plackett–Luce framework. Unlike previous studies that focus on individual effects with fixed covariates, our model allows covariates to vary across comparisons. This added flexibility enhances model fitting but also brings significant challenges in analysis. This article addresses these challenges in the context of maximum likelihood estimation (MLE). We first provide necessary and…

连续处理后变量的主层分析：非参数识别与半参数估计AI 解读

Abstract Post-treatment variables often complicate causal inference. They appear in many scientific problems, including non-compliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the…

主分层因果中介分析的识别与多重稳健估计AI 解读

Abstract We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply…

具有无限维固定效应的半参数面板多项选择模型识别AI 解读

Abstract This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indices, and uses the logical contraposition of an intertemporal inequality on choice…

带外生性预检验的识别稳健两阶段bootstrap检验AI 解读

Pretesting for exogeneity has become routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares or IV-based method is appropriate. Guggenberger (2010a, Econometric Theory , 26, 369–382) shows that the second-stage test – based on the outcome of a Durbin-Wu-Hausman-type pretest in the first stage – exhibits extreme size distortion, with asymptotic size equal to 1 when the standard critical values are used. In…

聚类相依下的稳健工具变量推断AI 解读

Summary Linear instrumental variables (IV) models with clustering dependence are widely used in empirical studies, although the common solution, the cluster covariance estimator, often produces undesirable inferential results, especially with weak instruments. In this paper, I propose a method that is robust to both weak IV and (potentially heterogeneous) clustering dependence. The proposed method is based on the idea of Fama–MacBeth estimation, with group-level estimators…

存在内生性时 probit 与 tobit 模型的边际效应AI 解读

Summary When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in non-linear models. We study this issue, focusing on the instrumental variable (IV) probit and tobit models. We show that even when a valid instrumental variable is available, failing to differentiate between the two types of endogeneity can lead to…

通过代理变量识别信念AI 解读

Abstract It is well-known that individual beliefs cannot be identified using traditional choice data, unless we exogenously assume state-independent utilities. In this paper, I propose a novel methodology that solves this long-standing identification problem in a simple way. This method relies on extending the state space by introducing a proxy, for which the agent has no stakes conditional on the original state space. The latter allows us to identify the agent’s conditional…

风险厌恶竞标者与选择性进入下第一价格拍卖的识别与推断AI 解读

Abstract We study identification and inference in first-price auctions with risk-averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming exogenous variation in either the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This…

通过因果表征学习构建可解释的深度生成模型AI 解读

Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods’ surprising performance is due in part to their ability to learn implicit “representations” of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to…

深度聚类评估：如何验证内部聚类评价指标AI 解读

Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: (a) the curse of dimensionality when applied to the high-dimensional input data, and (b) unreliable comparison of…

未测量混杂下的连续行动强化学习AI 解读

This article addresses the challenge of offline policy learning in continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework.…

深度离散编码器：具有离散潜在层的可识别深度生成模型AI 解读

In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose interpretable deep generative models for rich data types…

SMART-MC：基于协变量的多发性硬化治疗转换模型AI 解读

Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heterogeneous treatment responses, differences in disease progression, patient characteristics, and therapy-associated adverse effects. To investigate how patient-level covariates influence the likelihood of treatment transitions among DMTs, we adopt a Markovian framework, Sparse Matrix Estimation with…

最优治疗规则的改进界与推断AI 解读

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, informative bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, they can guide practical decisions based on formal decision theoretic criteria. Here we derive new results on optimal treatment regimes in settings where the effect of interest is bounded. These results are…

大型加性潜变量模型的统计分位数学习AI 解读

Large and complex datasets, emerging from technological advancements in fields such as genomics and brain imaging, hold ample promise for gaining new scientific insights. Yet, their inherent nonlinearity and high dimensionality present considerable theoretical, methodological, and application challenges to the statistics and machine learning community. This article introduces Statistical Quantile Learning (SQL), a new nonparametric method for estimating large additive latent…