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非随机缺失协变量回归的有限样本 Hausdorff 界与 Hadamard 设计敏感性AI 解读

Covariates missing not at random generally prevent point identification of regression coefficients without untestable restrictions. This paper studies linear regression when every missing covariate is restricted to a prespecified compact interval. The resulting population target is the set of best linear predictor coefficients compatible with the observed-data law. Under a non-atomic observed-data law and the stated regularity conditions, we can represent this identified set…

下尾Spearman相关曲线的Bernstein平滑估计与自助推断AI 解读

This paper studies a Bernstein-smoothed plug-in estimator for the lower-tail Spearman's rho curve, a rank-based measure of local concordance defined through a normalized copula integral over the lower-left square $[0,p]^2$. The estimator applies the lower-tail Spearman functional to the empirical Bernstein copula and introduces a degree parameter that controls finite-sample regularization. The contribution is target-specific estimation and inference for the lower-tail…

具有分布无关安全保证的保形政策学习AI 解读

Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the average outcomes alone may not be sufficient: decision makers may also seek to protect individuals from harm, in line with the Hippocratic principle of ``do no harm.'' In this paper, we propose \textit{conformal policy learning} (CPL), a policy learning procedure with a new…

因果推断中双重稳健双重／去偏机器学习估计的协变量选择AI 解读

High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facilitates the use of machine learning (ML) for causal inference by mitigating regularization and overfitting bias, but comparatively less attention has been given to covariate selection in relation to the double robustness (DR) property possessed by some DML…

动态选择下的因果推断：时变协变量与潜在异质性AI 解读

I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent…

复合自适应实验中的经验贝叶斯方法AI 解读

We investigate Empirical Bayes (EB) methods in the context of compound adaptive experiments, where the arm distribution in each experiment follows a normal distribution with an unknown mean that we seek to estimate. There are two main EB strategies: $g$-modeling, which estimates the prior by maximizing the marginal likelihood, and $f$-modeling, which derives posterior means directly from the empirical distribution of the observations. We show that $g$-modeling continues to…

不依赖收敛速度设定的成对数据回归 Wald 推断AI 解读

This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $χ^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no…

通过变换后的低秩分位数曲面发现因果方向AI 解读

We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank functional decomposition. LRQS subsumes location-scale noise models and post-nonlinear heteroscedastic noise models, while allowing multiple quantile bases to represent changes beyond location-scale effects. We prove generic identifiability of LRQS: the transformed quantile surface…

类别数据的稳健估计与推断AI 解读

Categorical data pose a distinctive robustness challenge: contingency-table cells need not have a meaningful magnitude, ordering, or metric, so departures must instead be assessed through discrepancies between observed cell frequencies and model-implied probabilities. We develop $C$-estimation, a unifying framework for robust estimation in structured categorical models. $C$-estimation limits the influence of large frequency discrepancies and can be applied to unconditional,…

预测评分分解的统计推断AI 解读

We introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable components: miscalibration, discrimination, and uncertainty. Our estimation and inference relies on a linear recalibration of the forecasts and is applicable to general point forecasts such as means and quantiles due to its validity for non-smooth scoring functions. This approach ensures non-negative decomposition terms in finite…

单因子稀疏投资组合选择的结构复杂性：精确算法、参数化困难性与受限电路下界AI 解读

We study exact-cardinality, equally weighted minimum-variance portfolio selection under a one-factor covariance model supplied in factor form. In the nonnegative homoskedastic regime, selecting the K smallest loadings is optimal. Allowing strictly positive asset-specific idiosyncratic variances makes the decision problem NP-complete even with positive integer loadings and a strictly positive-definite covariance matrix; with identity residual covariance, exactly one negative…

分层因果结构学习AI 解读

Traditional statistical approaches primarily model associations between variables, however many scientific and practical questions require causal methods instead. These methods typically rely on assumptions about an underlying structure and are often represented by a Directed Acyclic Graph (DAG). While causal structures can be learned for single-level data, hierarchical or multi-level settings, where units (e.g., plants, students or patients) are nested within groups (e.g.,…

偏态结局纵向临床研究中的灵活治疗效应推断框架AI 解读

Longitudinal continuous outcomes in clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) under normality assumptions. However, many clinical outcomes are skewed, making mean-based treatment effects difficult to interpret and potentially reducing statistical efficiency. The Box--Cox MMRM (BCMMRM) approach accommodates skewness by enabling inference on model-based median differences via inverse transformation. However, BCMMRM typically assumes…

函数值参数的广义投影检验及其在结构性因果假设检验中的应用AI 解读

Structural assumptions are central to the causal inference literature. In practice, it is often crucial to assess their validity or to test implications that follow from them. In many settings, such tests can be framed as evaluating whether a function-valued parameter equals zero. In this paper, we propose a class of generalized projection tests based on series estimators for function-valued parameters. We establish conditions under which the proposed tests are valid and…

生成式人工智能辅助的非结构化数据动态因果推断AI 解读

A growing number of scholars seek to estimate causal effects of unstructured data such as text, images, and video. However, existing methods typically treat each object as a single, static observation. We develop a statistical framework for dynamic causal inference with unstructured data by leveraging generative artificial intelligence (GenAI) models. Our approach enables researchers to estimate the causal effects of sequences of treatment features, including their positions…

空间依赖的印度自助过程AI 解读

We develop a new stochastic process called spatially dependent Indian buffet processes (sIBP) for binary feature matrices of unbounded columns with spatial correlations between subjects, and propose general spatial factor models for various multivariate response variables. We introduce spatial dependency through the stick-breaking representation of the original Indian buffet process (IBP; Griffiths and Ghahramani, 2005, 2011) and latent Gaussian process for the…

二变量函数型因果发现的统计推断AI 解读

Causal discovery methods aim to determine the causal direction between variables using observational data. Functional causal discovery methods rely on structural and distributional assumptions to determine directionality but typically lack statistical inference. This paper reviews the statistical guarantees of existing functional causal discovery methods in theory, software, and applied use, highlighting a key gap: the absence of a unified inferential framework that applies…

多尺度混杂与测量误差下融合扰动实验和人群单细胞数据的因果路径分析AI 解读

Single-cell perturbation experiments provide causal information on gene regulation, whereas population-scale single-cell studies characterize gene expression and phenotypes in human populations. We develop a framework that integrates these complementary data sources for causal path analysis. Rather than assuming that a perturbational gene network transfers directly to the target population, we use externally learned ancestral relationships to constrain the network topology…

人为强迫、气候变化与变暖形态：分布协整的统计推断AI 解读

Anthropogenic forcing components follow different long-run paths, while persistent temperature change can involve distributional changes beyond the mean. Scalar regressions aggregate these components and retain only mean temperature, obscuring how distinct forcing paths relate to persistent distributional change. We develop new testing, estimation, and inference methods for long-run relations between an integrated predictor vector and a density-valued response. These…

非参数贝叶斯政策学习AI 解读

I propose Nonparametric Bayesian Policy Learning (NBPL) as a framework for uncertainty-aware treatment choice. The key observation is that welfare is fully determined by a reduced-form distribution, so uncertainty about optimal policies entirely reflects uncertainty about this distribution. NBPL places a Dirichlet process prior on the reduced-form distribution and uses the resulting posterior for both traditional policy choice and inference on optimal welfare and optimal…

风险比较定理及其在扩散系数深度学习中的应用AI 解读

We investigate the nonparametric estimation of the diffusion matrix in stochastic differential equations featuring multidimensional, strong mixing covariate processes. We propose a flexible statistical framework based on general function classes that does not require a linear basis representation, rendering our results directly applicable to deep neural network estimators. Our approach employs a two-step estimation procedure: constructing a preliminary nonparametric…

CausalSmith：以形式化验证为基础的自我改进因果推断研究代理框架AI 解读

Automating theoretical research requires generating candidate results and evaluating them reliably. Models keep getting better at the first, while the second remains hard. A common approach asks one large language model (LLM) to review what another produced, yet such reviewers are empirically unreliable: they may accept fabricated papers and catch the fabrication at close to chance rates~\citep{badscientist2025}. We present \textsc{CausalSmith}, a framework for automated…

连续处理双重差分中的水平与响应两种边际AI 解读

This paper studies difference-in-differences with staggered adoption and a continuous, time-invariant dose. Each cohort-time comparison contains two margins. The level margin is the average treatment effect at realized doses. Under level parallel trends it equals the level contrast between the treated cohort and not-yet-treated controls. The response margin is the within-cohort slope of the outcome change on dose. It uses no controls, and its causal interpretation requires a…

非可分双边关系回归AI 解读

This paper studies a nonseparable model for dyadic outcomes, such as bilateral trade flows, in which the outcome depends on both agents' characteristics and on a scalar unobservable through an unknown function increasing in that unobservable. I establish identification of a normalized structural function and the error distribution, propose kernel plug-in estimators, and derive a two-regime central limit theory under dyadic dependence in which a shared-agent variance…