论文检索

局部差分隐私下利用交互机制进行非参数谱密度估计AI 解读

We study the problem of estimating the spectral density of a centered stationary Gaussian time series under local differential privacy constraints. Specifically, we propose new interactive privacy mechanisms for three tasks: recovering a single covariance coefficient, recovering the spectral density at a fixed frequency, and global recovery. Our approach achieves faster rates through a two-stage process: we first apply the Laplace mechanism to the truncated value, and then…

贝叶斯加性回归树的稳健半参数推断AI 解读

Bayesian additive regression trees (BART) provide a class of flexible nonparametric regressions in machine learning. Their rich tree-based structure can generate non-Donsker classes of regression functions. We study posterior inference for the population mean under missing at random. We derive a new Bernstein-von Mises (BvM) theorem showing that even a one-step posterior based on BART and Bayesian-bootstrap reweighting retains a non-negligible bias term in the non-Donsker…

粒子系统中扩散交互函数的非参数估计AI 解读

We study the problem of nonparametric estimation of the interaction function in particle systems whose interaction enters through the diffusion coefficient. The particle system is observed discretely over a fixed time interval, and the interaction function is assumed to belong to a suitable nonparametric function class. We propose an estimation procedure based on empirical risk minimization over finite-dimensional sieve spaces, introducing a computable contrast function that…

逆向混杂分析：精确量化混杂影响AI 解读

The presence of unmeasured confounding factors during the collection of observational data may lead to biased estimates of the effect of an exposure on an outcome. Consequently, a central problem in causal inference based on observational data is sensitivity analysis with respect to unmeasured confounding. Existing sensitivity analyses generally focus on worst-case bounds. We propose an exact method for quantifying the significance of confounding, defined here in terms of…

NIRVAR：网络信息约束向量自回归AI 解读

High-dimensional panels of time series often arise in finance and macroeconomics, where co-movements within groups of panel components occur. Extracting these groupings from the data provides a coarse-grained description of the complex system in question and can inform subsequent prediction tasks. We develop a novel methodology to model such a panel as a restricted vector autoregressive process, where the coefficient matrix is the weighted adjacency matrix of a stochastic…

将随机试验的相对效应汇总量迁移至已治疗患者：乳腺癌内分泌治疗应用AI 解读

Randomised trials often report relative treatment effects, such as risk ratios and hazard ratios, for trial populations. Clinical decision-making, however, often benefits from estimates of absolute treatment effects in the population eligible for treatment. Trial participants may not represent this target population well, and restrictions on access to individual participant trial data can further complicate absolute effect estimation. Routine care data are often…

长话短说：因果机器学习中的遗漏变量偏误AI 解读

We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts. Our theory applies to nonparametric models, while naturally allowing for (semi-)parametric restrictions (such as partial linearity) when such assumptions are made. We show how simple plausibility judgments on the maximum…

通过LOCO引导的自适应小子样本抽样进行模型无关特征选择AI 解读

Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging. Existing model-agnostic methods primarily estimate feature importance or conduct inference on it rather than directly selecting features, whereas many feature selection methods are model-specific or rely on the model-X assumption. We introduce LOCO-guided Adaptive Minipatch Sampling (LAMPS), a…

协变量完全缺失时广义线性回归的双重稳健目标总体推断AI 解读

Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population inference for generalized linear regression when key covariates are completely absent from the target data but observed in a related source population. Standard missing covariate methods are not directly applicable because they require at least partial observation of the covariates in the target population. We develop a doubly robust…

大规模BLP估计的随机嵌套不动点算法AI 解读

We develop a stochastic nested fixed point (SNFP) estimator for random coefficients logit demand models that updates model parameters using stochastic gradients and performs demand inversion one market at a time. Relative to the conventional nested fixed point (NFP) estimator, SNFP substantially reduces memory requirements and computational cost, making estimation feasible in very large datasets. We establish the large-$T$ (number of markets) asymptotic properties of the…

利用代理变量调整的靶向干预因果发现AI 解读

Randomized perturbations can reveal downstream responses without identifying which causal relationships are direct. Conditioning on intermediate responses may induce associations through unmeasured common causes, even when the source is randomized. We introduce Proxy-Adjusted Balanced Masking (PABM), a nonparametric framework for recovering directed acyclic graphs from targeted interventions, response data, and recorded proxies. PABM uses valid target-specific interventions…

稳定化高阶影响函数：一类双线性形式的统计理论AI 解读

Higher-order influence functions, introduced in a series of articles (Robins et al., 2008, 2009a; van der Vaart, 2014; Robins et al., 2016, 2023; Liu et al., 2017), are a unified framework for constructing rate-optimal point estimates of a class of statistical functionals under various complexity-reducing assumptions on the posited statistical model that generates the observed data. Although higher-order (influence functions) estimators are theoretically appealing, they have…

调查实验的精细分层AI 解读

This paper studies a two-stage model of experimentation, where the researcher first samples representative experimental participants from an eligible pool, then assigns each sampled unit to treatment or control, using matched $k$-tuples randomization at both stages. To implement such designs, we develop a fast algorithm for matching units into $k$-tuples for any $k \ge 2$ and any dimension of covariates. By surveying 200 recent experimental working papers, we estimate that…

利用机器学习实现分布平衡：以真实世界数据增强临床试验AI 解读

In clinical trials, randomization of treatment and control groups is typically used to ensure the groups have similar covariate distributions on average, resulting in unbiased causal effect estimation. Such balanced covariate distributions are hard to achieve in practice, however, due to recruitment costs, patient dropouts, and more. One possible solution to this problem is to include control patients from external, real world databases. In this paper, we propose DBML, a…

tteICE：估计两组试验中伴随中间事件的事件时间处理效应的 R 包AI 解读

Evaluating treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events (ICEs), e.g., treatment discontinuation or competing events. While the ICH E9 (R1) addendum outlines five strategies to address these challenges, accessible software implementing these methods is limited. This article introduces the \pkg{tteICE} package for \proglang{R}, which implements five strategies to facilitate valid causal inference in the presence of…

基于扰动方法的稳健滤波与平滑AI 解读

Using a perturbation technique, we derive a new approximate filtering and smoothing methodology generalizing along different directions several existing approaches to robust filtering based on the score and the Hessian matrix of the observation density. The main advantages of the methodology can be summarized as follows: (i) it relaxes the critical assumption of a Gaussian conditional distribution for the latent states underlying such approaches; (ii) can be applied to a…

黎曼流形上的自回归过程AI 解读

This paper introduces a Riemannian autoregressive (R-AR) model of order one for manifold-valued time series. The model is specified through an autoregressive process in the tangent space at a reference point, which is mapped to the manifold via the exponential map. It is characterised by two parameters: a reference point $μ$, representing the intrinsic central tendency through the Fréchet mean, and an autoregressive parameter $φ$, governing the dependence structure and…

通过邻域排除交叉拟合实现网络干扰下的无偏处理效应估计AI 解读

Without interference, cross-fitting enables flexible covariate adjustment while preserving finite-sample unbiasedness under independent unit-level randomization. Under network interference, out-of-sample prediction alone no longer guarantees unbiasedness: assignments entering evaluation-fold Horvitz--Thompson weights may also affect outcomes in the training sample, inducing dependence between fitted predictions and those weights. We develop neighborhood-excluded…

超越单调性的异质幸存者平均因果效应：机械通气临床试验应用AI 解读

Clinical trials in critical care often evaluate outcomes that are truncated by death, such as time to discharge alive, for which treatment effects are not well defined among patients who would not survive under one or both treatment strategies. Principal stratification provides a natural framework for defining survivor causal effects, but existing approaches often rely on monotonicity assumptions that may be implausible in settings where treatment can affect survival in…

向量值分布强化学习的策略评估：希尔伯特空间嵌入方法AI 解读

We propose Kernel Embedding Distributional Reinforcement Learning (KE-DRL) an offline method to estimate kernel mean embeddings of conditional multivariate return distributions from observed offline trajectories with continuous state-action inputs and vector-valued rewards. KE-DRL approximates the conditional embedding and estimates its a finite-dictionary representation coefficients through a maximum mean discrepancy Bellman criterion. For a regular class of return…

基于大语言模型文本测量的再犯预测、同伴效应估计与预测辅助推断AI 解读

We provide a new framework for estimating peer effects when outcomes are multivariate behavioral measures derived from written text using an LLM and the network formation is endogenous. We obtain LLM embeddings and zero shot classification of more than 200,000 written exchanges among residents of low-security correctional facilities. We find that LLM embeddings improve out-of-sample recidivism prediction by up to 30% over pre-entry covariates alone using LASSO and LoRA…

超高维回归中最大检验校准的 Stein 收缩与诚实子模型选择AI 解读

Classical preliminary-test and Stein-type estimators interpolate between restricted and full regression fits, but OLS and chi-squared calibration fail when $p\gg n$ and the restriction is data-adaptive. We propose an honest sample-separated framework built around a common selected-null law. Independent selection data extend a mandatory core; separate estimation data pair a complete-design regularized full-model (FM) fit with a fresh exact-null submodel refit. A maximum…

多种处理版本下的识别与估计：混合专家方法AI 解读

The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment. It has been pointed out that ignoring such multiple versions of treatment can lead to biased estimates of causal effects, but a causal inference framework that explicitly deals with the unbiased…

两阶段抽样下加权处理效应的有效估计AI 解读

Two-phase sampling offers a practical way to collect costly confounders only in a subsample while retaining inexpensive information for a larger cohort. In observational causal studies, however, phase-2 selection can distort estimation of population causal effects if the sampling mechanism is ignored, and available phase-1 information may also be exploited to improve efficiency. Yet efficiency theory for causal estimands under such designs remains limited, particularly…