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有序结果下的正交政策学习AI 解读

Policy learning methods based on conditional average treatment effects can obscure subpopulation heterogeneity when applied to ordinal outcomes. We develop a policy learning framework for ordinal outcomes with heterogeneous utilities for individuals who strictly benefit from treatment and those who do not. Since the probability of strict benefit is only partially identified without further conditions, we adopt a minimax strategy which minimizes the worst-case regret over the…

不可忽略流失与补充抽样面板模型中的比例迭代估计与推断AI 解读

In panel data subject to nonignorable attrition, auxiliary (refreshment) sampling may restore full identification under weak assumptions on the attrition process. Despite their generality, these identification strategies have seen limited empirical use, largely because the implied estimation procedure requires solving a functional minimization problem for the target density. When the distributions of the observed data are parametric, we show that this problem can be solved…

利用工具变量推断广义主分层条件下的处理效应AI 解读

We propose a general approach to inference for a broad class of models that arise in the analysis of treatment effects with discrete-valued treatments and instruments and a general-valued outcome. In addition to instrument exogeneity, the main substantive assumption in our class of models rules out certain response types by assuming that they occur with probability zero. Here, the response type refers to the vector of potential outcomes and potential treatments, and we refer…

局部差分隐私下渐近随时有效的分位数推断AI 解读

Sequential quantile inference is difficult under local differential privacy because every record is randomized before reaching the analyst and the limiting quantile variance depends on an unknown density. We develop an online procedure that combines randomized response with dynamically chained parallel stochastic gradient descent (P-SGD). The resulting Polyak--Ruppert estimator admits a strong Gaussian approximation. A cross-chain quadratic statistic, computed entirely from…

隐私保护空间自回归模型的偏差校正与餐馆网络应用AI 解读

Spatial autoregressive (SAR) models and their extensions are important tools for studying network effects. However, with an increasing emphasis on data privacy, data providers often implement protection measures that render standard SAR models inapplicable. In this study, we introduce a privacy-protected SAR model that incorporates noise into both the response and covariates to meet privacy requirements. With noise present in both components, the traditional quasi-maximum…

未知高斯噪声下的流形假说：条件性证书与一致维数估计AI 解读

We study what noisy data can establish about the Manifold Hypothesis under explicit identification and regularity conditions. A population residual certificate combines independent-view localization, Gaussian concentration, membership uncertainty, and population transfer. Existing rectifiability criteria then yield a covered-scale consequence. For a local smooth manifold with positive Hölder density, the actual-ball covariance limit identifies the spectral crossing with…

连接局部与总体因果效应：近端工具变量方法AI 解读

Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument generally identifies the local average treatment effect (LATE) among compliers rather than the population average treatment effect (ATE). When treatment effects and compliance probabilities are heterogeneous and dependent through latent factors, the ATE need not be identified by IV variation alone. We develop a proximal…

用贝叶斯加性回归树评估观察数据中的治疗获益预测器AI 解读

A treatment benefit predictor (TBP) is an algorithm that maps a patient's characteristics to their putative benefit from a given treatment, which can be used to inform treatment decisions. However, a TBP must be evaluated in the target population before being adopted for patient care. When only observational data are available, evaluating TBPs requires standard causal identification assumptions, as treatment assignment in such settings is not random. We obtain the posterior…

多重样本选择下的泊松回归AI 解读

This paper develops a Poisson regression model with multivariate sample selection, in which the outcome is observed only when several potentially correlated selection conditions are satisfied. To the best of our knowledge, this is the first Poisson sample selection model that allows for an arbitrary number of selection equations. We derive the conditional mean of the observed outcome under joint normality of the outcome and selection errors and obtain a multivariate…

Lévy 噪声下广义朗之万方程的历史条件化记忆与随机驱动推断AI 解读

We consider the identification of memory and stochastic forcing in a one-dimensional generalized Langevin equation with state-dependent Brownian noise and optional additive symmetric $α$-stable jumps. A finite-dimensional Markov embedding provides Kramers--Moyal conditional moment relations that separate memory drift, Brownian diffusion, and jump contributions. From increment ensembles conditioned on the observed velocity history, truncated statistics estimate the stable…

流行病学因果图识别：挑战、可识别性与算法AI 解读

Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets containing ordinal scales, counts, and continuous measurements. This paper investigates causal discovery in Directed Acyclic Graphs (DAGs) where nodes follow…

非随机缺失数据下因果目标量的识别与估计AI 解读

Missing not at random (MNAR) data pose significant challenges for causal inference, particularly when both confounders and the outcome are partially observed. Without additional assumptions beyond those required for causal inference, causal estimands are generally not identifiable under MNAR mechanisms. This paper first develops identification results for the causal estimand, the average treatment effect, under several plausible MNAR mechanisms using completeness conditions,…

超越行动者—伴侣关联：纵向双人数据因果溢出效应的目标试验框架AI 解读

Actor-partner interdependence models (APIMs) describe how one member's exposure is associated with that member's and the partner's outcomes, but actor and partner coefficients are causal only under additional design and identification assumptions. We formulate APIM-style questions as dyadic target trials under within-dyad interference and make three links between the associational and causal formulations explicit. First, under joint exchangeability, positivity, and a…

线性模型中的内生异方差AI 解读

Linear regressions with endogeneity are widely used to estimate causal effects. This paper studies a framework that involves two common practical issues: endogeneity of the regressors and heteroskedasticity that depends on endogenous regressors, i.e., endogenous heteroskedasticity. To address the inconsistency of the two-stage least squares estimator in this scenario, and recover the causal parameters of interest, we develop a framework for practical estimation and inference…

向量值核岭回归的稳健误差界AI 解读

While kernel-based learning methods for scalar-valued functions have been extensively studied, comparatively little work considers nonlinear vector-valued operators. Such operators with vector-valued outputs, e.g., state vectors and trajectories, commonly arise in system identification problems. In this paper, we study the problem of learning a vector-valued operator under the framework of vector-valued kernel ridge regression. We assume that the outputs take values in an…

审查员工具变量设计的局部稳健半参数方法AI 解读

I propose a locally robust semiparametric framework for estimating causal effects using examiner IV designs when adjustment for many or continuous covariates makes saturation infeasible or produces sparse cells. The key ingredient of this approach is an orthogonal moment function that removes the first-order effect of estimation errors in the two treatment regressions defining the examiner IV. I derive the orthogonal moment function and show that it remains valid under…

断点回归设计中外推治疗效应的界AI 解读

External validity is a central concern in regression discontinuity (RD) designs. To assess treatment effect heterogeneity and the representativeness of the conventional RD estimand, this paper studies the identifying content of monotonicity and smoothness restrictions for treatment effects away from the cutoff in both canonical and multi-cutoff RD designs. In the canonical design, monotonicity in the running variable alone has limited identifying power and generally yields…

带工具变量的收缩贝叶斯因果森林AI 解读

Discovering interpretable subgroups whose complier effects deviate from the average is a central goal of instrumental variable analysis under imperfect compliance, yet existing tree-based methods degrade when most covariates are irrelevant to the effect. We propose Shrinkage Bayesian Causal Forest with Instrumental Variable (SBCF-IV) for discovering and estimating subgroups with heterogeneous Complier Average Causal Effects (CACE) in sparse high-dimensional settings. SBCF-IV…

生存治疗效应的有效迁移与推广AI 解读

Randomized controlled trials provide internally valid estimates of treatment effects, but their results may not directly apply to broader target populations due to differences in baseline covariate distributions, adherence to treatment or variations in outcome mechanisms. Under standard transport and time-to-event identifiability assumptions, we develop nonparametric, debiased machine learning estimators for transporting and generalizing causal survival treatment effect…

适用于多资产的计算高效多元波动率模型AI 解读

This paper develops a flexible and computationally efficient multivariate volatility model that accommodates dynamic conditional correlations and volatility spillover effects among financial assets. The new model has desirable properties such as identifiability and computational tractability for many assets.A sufficient condition for strict stationarity is derived for the process.Two quasi-maximum likelihood estimation methods are proposed for the new model without and with…

正则化似然反卷积与后验密度重构AI 解读

Density deconvolution is an ill-posed inverse problem because recovering the latent density amplifies high-frequency variation in the observed data. We propose a two-stage likelihood procedure. The first estimator maximizes the convolution likelihood over a regularized Gaussian-mixture sieve; the second treats this fit as an empirical prior and averages the fitted conditional latent densities. Under fixed-model misspecification, the estimators generally have different…

伪增量检验：利用自然发生的干预衡量广告增量效应AI 解读

We develop a method for measuring the incremental effect of advertising when randomized exper- iments are unavailable. Firms generate abrupt interventions in their own marketing as a byproduct of operations: budgets are cut, channels launch, programs pause. We propose a two-stage proce- dure that treats these events as quasi-experiments. The first stage discovers and dates interventions by exact Bayesian run-length inference on marketing activity series. The second stage…

第一阶段无法检测什么：线性工具变量中的函数形式污染AI 解读

Applied instrumental variables (IV) practice reports a first-stage F, now often the conditional F of Sanderson and Windmeijer (2016), and reads a large value as license to interpret the second stage. We show that no first-stage diagnostic can provide it. With a scalar instrument, a scalar treatment, and covariates entered linearly, the 2SLS estimand splits into a signal that a saturated specification would target and a contamination, the covariance between curvature in the…

Tensor-BEKK：张量时间序列的条件协方差建模与推断AI 解读

Modern economic and financial data are increasingly organized as multiway arrays, with observations indexed simultaneously by geographic regions, industrial sectors, asset categories, and other economic characteristics. Representing such data as tensor-valued time series preserves their intrinsic multiway structure. Although substantial effort has been devoted to modeling the conditional mean of tensor-valued time series, comparatively less attention has been paid to their…