每日简报

融合随机试验与真实世界数据的生存异质处理效应贝叶斯森林AI 解读

We develop the Bayesian fusion forest, a nonparametric framework to estimate heterogeneous treatment effects on survival outcomes by combining a randomised controlled trial and real-world data. The framework relaxes the unconfoundedness assumption on the real-world data by assuming instead that the treatment effect transports across the two sources. Our method opens up right- and interval-censored outcomes to data fusion. We model the survival time with an accelerated…

跨单位溢出的非参数检验AI 解读

Cross-unit dependence is pervasive in empirical applications and complicates econometric inference, especially when spillovers operate in nonlinear ways. We propose a novel nonparametric test for cross-unit spillovers that may operate through peers' attributes, peers' outcomes, or both. The test is straightforward to implement, as it requires only estimation under the null hypothesis of no cross-unit spillovers, and is shown to have a convenient asymptotic standard normal…

We study nonparametric inference for a continuous-treatment causal dose-response curve and its first derivative under unmeasured confounding, using treatment- and outcome-inducing proxies. We establish a proximal doubly robust pseudo-outcome whose conditional mean given treatment equals the dose-response curve whenever either bridge function is correctly specified. Their (smoothed) efficient influence function representations lead to cross-fitted local-polynomial estimators…

随机对象的单调处理反应与部分识别AI 解读

Monotone treatment response (MTR), monotone treatment selection (MTS), and monotone instrumental variable (MIV) assumptions are widely used to partially identify counterfactual mean outcomes, but existing analyses have focused almost exclusively on scalar outcomes. We develop a unified framework for partial identification with outcomes that take values in a general metric space under these monotonicity restrictions by embedding the metric space into an $L^2$ space and…

事件时间结果因果亚组估计的贝叶斯层级自助框架AI 解读

Causal estimation of treatment effects within prespecified subgroups, such as biomarker-defined strata, disease phenotypes, or demographic groups are often of clinical interest. Bayesian approaches are attractive for subgroup effect estimation because flexible priors can represent complex treatment heterogeneity and propagate posterior uncertainty. Frequentist methods for prespecified causal subgroup analysis with time-to-event outcomes are also available, including…

高维交互固定效应下的双重机器学习AI 解读

Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE). Existing IFE estimators rest on low-dimensional and linear specifications in the covariates, assumptions which are increasingly restrictive in applications drawing on rich datasets with controls of unknown functional form. This paper develops a Double Machine Learning estimator for the…

观察性多层网络数据的回归估计AI 解读

A novel method to estimate social effect coefficients in the popular so-called linear-in-means regression model in the Social Sciences is presented here that utilizes non-experimental multidimensional network data. The procedure can accommodate social interactions that correlate with the error in the model by making use of a different set of network links among the same observations that are exogenous in the traditional sense. In particular, the full observability of a…

小样本与短面板：用贴近实际的数据评估政策评价方法AI 解读

Methods for estimating causal effects in longitudinal, quasi-experimental settings are widely used in economics, public health, political science, and other fields. However, studies evaluating effects of health policies often rely on limited sample sizes, both in terms of study units and time periods analyzed. Synthetic difference-in-differences (SDiD) and the augmented synthetic control method (ASCM) are recently developed methods for evaluating the effects of policy…

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…

成对数据回归模型的设定检验AI 解读

This paper develops omnibus specification tests for linear conditional-mean models with undirected dyadic data. We establish a uniform projection theorem that reduces the dyadic process to its latent first-order node projections under shared-node dependence. We then show that a raw first-order node-multiplier bootstrap is valid when this node component is nondegenerate but double-counts dyad-specific variation when dyads are independent. An exact covariance decomposition…

网络中的测量误差与同伴效应AI 解读

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need…

拓扑因果数据分析的数学框架AI 解读

Many modern outcomes, including images, point clouds, networks, and spatial fields, are structured objects for which \(Y^1-Y^0\) may be undefined or scientifically inadequate. We introduce \emph{Topological Causal Data Analysis} (TCDA), a framework separating the observation space, causal-model class, topological representation, and causal query. Topology does not define interventions; it supplies stable, shape-sensitive summaries after causal assumptions have been…

动态线性面板模型的 Arellano–Bond LASSO 估计量AI 解读

The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. It can be severely biased when the time series dimension of the data, $T$, is long. The source of the bias is the large degree of overidentification. We propose a simple two-step approach to deal with this problem. The first step applies LASSO to the cross-section data at each time period to select the most informative moment conditions, exploiting the approximately…

示证型实验AI 解读

Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatment effect but to demonstrate that at least one candidate intervention yields a positive effect, for some subpopulation and on some measured outcome. We formalize this objective as testing the global null in a threshold bandit framework, and develop two inference procedures that are valid under…

去中心化网络上的分布式卷积秩回归AI 解读

This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework, in which estimators are obtained by solving consensus-constrained optimization with kernel-smoothed rank loss. The developed estimation scheme relies solely on local node data and information shared by neighboring nodes, thereby achieving privacy preservation and high communication efficiency. For heterogeneous network…

高斯图模型下网络复杂度的估计AI 解读

The proportion of edges in a Gaussian graphical model (GGM) characterizes the complexity of its conditional dependence structure. Since edge presence corresponds to a nonzero entry of the precision matrix, estimation of this proportion can be formulated as a large-scale multiple testing problem. We propose an estimator that combines p-values from simultaneous edge-wise tests, conducted under false discovery rate control, with Storey's estimator of the proportion of true null…

条件函数图模型中识别差异网络的近邻选择方法AI 解读

Estimating how different brain regions communicate with each other using EEG data is valuable both for medical research and clinical diagnosis. This involves quantifying the statistical dependencies among the activities of different brain areas, captured by the time-varying electric field recorded by scalp sensors. These dependencies can vary within and across individuals also in relationship with external factors such as age, mental state, or disease severity. Motivated by…

MOCA：采用单向交叉注意力与反馈切断的模块化因果推断框架AI 解读

Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting can perform well under favorable model specification but may become unstable in complex settings. Machine-learning and representation-learning methods provide greater flexibility, but joint optimization may allow outcome information to alter treatment representations and compromise…

学会到哪里寻找数据：用于政策选择与数据收集的 Delaunay 匹配AI 解读

This paper studies data-driven policy choices from a geometric perspective. A sample from a donor population fully exposed to an innovation informs a policymaker (PM) on whether to innovate groups in a target population where the innovation was not introduced. Any wrong decision must be compensated from a finite budget and the PM seeks an estimator of the innovation's effect that guarantees an affordable compensation cost. I focus on matching estimators with positive weights…

潜在分组可能无法分离时面板模型的稳健推断AI 解读

We develop robust inference methods for general linear hypotheses in linear panel data models with latent group structure in the coefficients. We employ a selective conditional inference approach based on the conditional distribution of coefficient estimates given the group structure estimated from the data. The resulting inference procedures remain valid even when group separation fails (i.e., when the distributional properties of the group-specific coefficients are not…

误设下溢出效应的分解：伪真估计目标与局部—全局扩展AI 解读

To measure spillovers, researchers often summarize who else was treated with a simple measure, such as the share of treated neighbors. We study what the researcher estimates when that summary is misspecified. We show that the researcher estimates the best approximation to the true policy effect among all functionals of the chosen summary, and that the usual direct and spillover estimates are exactly the components of this approximation. Under a monotonicity restriction, the…

剩余生存时间分位数的贝叶斯非参数因果推断：阿尔茨海默病应用AI 解读

In Alzheimer's disease research, a clinically important question is how much longer individuals would remain dementia-free beyond a given time under different baseline amyloid statuses. We address this question using observational data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) treating baseline amyloid status as the exposure. Estimation is challenging because amyloid status is confounded, time to dementia onset is heterogeneous and heavily right censored,…

基于微观数据的一致半非参数需求估计AI 解读

This paper develops a profiled sieve minimum-distance estimator for a semi-nonparametric differentiated-products demand model with micro-level choice data. Building on Berry and Haile (2024), the estimator uses within-market variation in consumer covariates to recover a flexible consumer-heterogeneity function and market-specific composite intercepts. Excluded price instruments then separate these intercepts into a flexible price-side function and structural demand shocks.…

序列相关下的稳健双样本推断AI 解读

We propose robust inference for two-sample comparison with time-series data under serial dependence and heterogeneous long-run variances. Standardizing with orthonormal-basis (series HAR) projections, we develop two-sample t-tests and, for joint hypotheses on a vector of means, a series HAR Wald statistic. Because the increasing-K chi-square limit tends to over-reject, we propose Welch-type fixed-K t- and F-approximations with adjusted degrees of freedom. We further develop…