每日简报

利用随机算法生成的数据估计因果效应AI 解读

Recommendation systems and chatbots present content to users, typically using stochastic algorithms that select the content based on user characteristics or context. Examples of content include chat responses, videos, or items available for purchase. Scientists and application developers are often interested in whether characteristics of content increase outcomes such as user engagement. Estimates of such causal effects may guide content providers to generate content that…

高维稀疏情境信息下组合优化器的学习后推断：最小方向扰动方法AI 解读

We study post-learning inference for structural properties of data-dependent combinatorial optimizers. The target is whether an oracle optimizer, rather than a latent parameter or smooth functional, belongs to a prescribed class, such as a category-mix, inventory, or resource-feasibility class. We focus on a high-dimensional contextual multinomial logit model with sequentially adaptive data collection, where the parameter-to-optimizer map is discontinuous and the policy…

暴露或结局罕见时哪种正则化倾向得分与双稳健方法校准最佳？基于代理混杂调整的plasmode研究AI 解读

Purpose. Confounding adjustment in health-care database studies screens large proxy libraries where events per variable are low, straining standard propensity score (PS) methods, and many regularized variable-selection strategies exist (outcome-adaptive LASSO [OAL], group LASSO/GLiDeR, highly adaptive LASSO [HAL]). Yet few comparisons have varied exposure prevalence within such a selection menu, and none pairs it with doubly robust estimation, compute accounting, and a…

比例高维下尺度混合误差的贝叶斯DAG结构学习与收缩协方差估计AI 解读

We propose a unified Bayesian framework namely robust DAG-Cholesky horseshoe (R-DACH) for joint directed acyclic graph (DAG) structure learning and precision matrix estimation in the high-dimensional proportional asymptotic regime $p/n \to c \in (0,\infty)$, under the scale mixture of normal errors. The construction places a global-local horseshoe-type prior directly on the strictly lower-triangular entries of the modified Cholesky factor of the DAG-Markov precision matrix,…

面向分布型数据的动态Fréchet回归与特征选择AI 解读

Many scientific and engineering applications generate responses that are not scalars or vectors, but statistical objects whose form evolves over an ordered index such as time, depth. Probability distributions are a prominent example, capturing variability and uncertainty that cannot be summarized by low-dimensional statistics. When such responses are observed sequentially, the resulting dynamic distributional trajectories pose significant challenges for regression,…

因果重叠效应：累积固定效应方法AI 解读

Social scientists often ask about the effect of increasing one's duration of exposure to a social context on one's outcomes, i.e. the overlap effect. Past studies adopted a unidimensional treatment effect framework to estimate the effect of overlap, imposing important restrictions. In this paper, we propose a new causal framework of multidimensional treatments where the overlap effects include both the duration and the content of overlap, under which, for instance, the…

利用边际矩信息识别并界定因果效应的中心矩AI 解读

Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (ICE) characterize the shape of the ICE distribution, thereby revealing the extent and structure of treatment effect heterogeneity across individuals. This paper investigates the identification and bounding of the central moments of the ICE using only…

复发事件结局的双重稳健方法：降压治疗对急性肾损伤的因果效应AI 解读

Evaluating the average causal effects of treatment strategies on recurrent event outcomes, such as heart attacks or renal failure, is important in clinical and medical research. However, the analysis becomes increasingly complex as multiple interacting factors are considered within a longitudinal setting. In this paper, we use advanced methodologies to estimate the average causal effects of standard versus intensive blood pressure-lowering therapies on acute kidney injury…

政权更替的实证全局博弈模型AI 解读

Global games theory provides a tractable framework for analyzing coordination problems with multiple equilibria, with regime overthrow serving as a canonical application. A large empirical literature on coups d'état examines the relationship between country-level characteristics, coup occurrence, and coup success using reduced-form approaches that leave the underlying coordination problem implicit. Bridging these literatures, we develop an estimable global games model of…

营销干预中贝叶斯因果影响模型的敏感性与早期检测AI 解读

Marketing systems frequently undergo operational changes that may affect performance, making timely detection of adverse effects essential for decision making. While Bayesian Causal Impact models are widely used to estimate the causal effects of interventions, their ability to support early operational monitoring remains less explored. This paper proposes a simulation based framework to evaluate the sensitivity and detection capabilities of Bayesian causal impact analysis…

二元结局处理效应的最优实验设计与稳健推断AI 解读

We study randomized experiments with binary outcomes under Neyman's nonparametric model, where covariate measurements are fixed but potential outcomes are random. In this setting we derive the exact variance of the difference-in-means estimator and characterize designs that minimize it. We show that any balanced design satisfying a covariate-balance condition is asymptotically optimal, and we prove that a broad class of blocking designs satisfies this condition under mild…

因子增强的机器学习面板回归AI 解读

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation…

兼容机器学习的处理效应异质性综合检验AI 解读

This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including modern machine-learning methods. The test is designed for settings in which identification can rely on high-dimensional controls while heterogeneity is assessed with respect to a low-dimensional subset of covariates. We derive the test statistic's asymptotic null distribution and develop a…

正视溢出：实验中的空间效应AI 解读

Interventions delivered in space generate effects that spill over between experimental units. We develop a framework for spatial experiments in which causal estimands, inclduing direct, indirect, total, and dose--response effects, are linear functionals of a spillover kernel. Under cluster randomisation with a fixed exposure set size, the data identify the shape of the kernel but not its level: the level is aliased with the intercept and direct effect, at any sample size and…

行为健康个案研究的因果推断AI 解读

We present a framework for causal inference in behavioral health case studies -- and observational N=1 settings more generally -- under unmeasured confounding. The framework rests on a class of causal estimands, termed $Ω$ estimands, defined as contrasts of functions of an outcome variable's support rather than its distribution. Because such estimands do not depend on how probability is distributed over supports, they are insensitive to the confounding that limits other…

聚类数据中的结构学习AI 解读

Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely homogeneous population, precluding their application to clustered data where cluster-specific variations (e.g., patient-specific effects) are common. We address this issue by introducing a new approach that estimates a global structure while accounting for local cluster-level effects. The key idea is…

网络元分析的因果视角AI 解读

Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches typically aggregate study-level treatment effects to obtain an overall estimate. We argue that the causal estimand should come first, with the aggregation derived only afterwards: the target population and the relevant sources of between-study heterogeneity should be explicitly defined before…

大规模关系状态过程的推断AI 解读

Relational states refer to concepts such as friendship or collaboration, in which a relationship persists over a certain amount of time. Study of relational states often involves figuring out what factors contribute to the creation or dissolution of these relationships. However, most methods available now restrict their attention to binary states, i.e., ties that are either present or absent, even though many real-world systems evolve through multiple relational states…

斯大林是否必要？苏联经济增长的反事实证据AI 解读

This article evaluates whether the exceptionally rapid growth of the Russian and Soviet economy after 1928 requires a specifically Stalinist explanation. The analysis treats this historical question as a sequence of increasingly demanding counterfactual comparisons rather than a binary comparison between observed growth and a single alternative. Long run output series from the Global Macro Database and the Maddison Project are combined with synthetic control, augmented…

混合解释变量下的误设回归：稳健推断与因果解释AI 解读

For analytic convenience, existing statistical frameworks either assume random or fixed regressors. However, it is a little awkward that they do not cover the practical case of estimating the average treatment effect in experiments with randomized treatments and non-randomized, fixed pretreatment covariates. We unify the literature by providing the theory for regressions with mixed regressors that contain both random and fixed components. Importantly, our theory allows for…

媒体效果衡量与“助攻乌龙”：归因、营销组合模型及个体增量效应AI 解读

We use the assisted own goal hypothesis as a lens into media measurement. A demand-generating (upper-funnel) advertising platform such as a short-video social network can cause an incremental purchase, yet see that purchase booked on -- and credited to -- a downstream trusted marketplace, because consumers who discover a product on the platform complete the transaction elsewhere, for example because of distrust of the generating platform as a psychological mechanism. Under…

附带参数问题的投影解法AI 解读

This paper introduces a new approach to econometric analysis of nonlinear panel data models when the number of observations per observational unit is small. In such models the presence of variables that are constant within, while varying across, units results in an incidental parameter problem. The approach taken in this paper removes these incidental parameters via projection, which produces a correspondence specifying all combinations of observed variables and…

通过拉普拉斯近似实现可扩展的贝叶斯有向无环图结构学习及乳腺癌应用AI 解读

Structure learning of directed acyclic graphs (DAGs) from observational data is a foundational task in causal discovery and is widely used to infer regulatory networks from medical and genomic measurements. The Bayesian formulation quantifies model uncertainty and admits prior biological knowledge, but its practical use has been hampered by the super-exponential growth of the DAG space and by the intractability of the node-marginal likelihood under flexible, non-conjugate…

通过 Riesz 表示构建现代因果面板估计量的贝叶斯稳健性值AI 解读

We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial…