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Sigmoid-FTRL：面向 AIPW 估计量的基于设计的自适应 Neyman 分配AI 解读

We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators for estimating the average treatment effect in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arrives, an adaptive procedure must select both a treatment assignment probability and a pair of linear predictors to be used in the AIPW estimator. Our goal is to construct an adaptive procedure that minimizes the Neyman Regret, which is the…

含大量生成控制变量的回归AI 解读

We consider linear regression when some covariates are endogenous, a variable sufficient to control for endogeneity exists but is latent, and the practitioner can generate many proxies for the latent control. For instance, one could cheaply but imperfectly impute the latent control using different AI/ML methods, each generating a different proxy. There are several options the practitioner might consider in this scenario: including all proxies or factors computed from the…

In many partially identified models, sharp bounds on structural or causal parameters are values of linear programs (LPs) with estimated coefficients. We introduce a debiased penalty estimator of the LP value. It is pointwise root-n-consistent whenever the population feasible set is nonempty and bounded. Although no estimator is uniformly consistent over the unrestricted collection of distributions, ours retains a one-sided guarantee: the extent to which it can overestimate a…

条件相似检验的近似极小极大遗憾AI 解读

We construct approximately minimax regret optimal tests in a scalar Gaussian instrumental variables experiment. This example shows how conditionally similar tests allow a novel approximation approach, allowing us to optimize weights over alternatives to minimize worst-case power regret. Stochastic mirror descent and averaging yield finite-iteration guarantees with exact conditional similarity. We establish bounds for exact and simulated powers and illustrate the procedure…

存在终止事件时复发事件回归模型的有效估计AI 解读

Recurrent events are common in biomedical studies, and efficient methods are needed to analyse them, particularly when a terminal event may occur. Regression modelling of the marginal mean of recurrent events provides a simply interpretable approach, but standard estimation based on inverse probability of censoring weighting may not fully exploit observed event histories in multiple events settings. We demonstrate how the popular Ghosh-Lin proportional means model can be…

已实现二次协变差与收益不对称性的非参数检验AI 解读

We propose a nonparametric test of return asymmetry based on the realized quadratic covariation between a cumulative return path and its square using high-frequency data. Our null hypothesis is that the volatility path is independent of the Brownian motion driving returns. Under this null and a martingale condition, the infill limit of the statistic is conditionally Gaussian given the volatility path, with a conditional variance determined by integrated variance.…

Uncertainty quantification for individual treatment effects (ITEs) is a daunting challenge in causal inference. Motivated by recent advances in conformal prediction, several works aim to construct distribution-free prediction sets for ITEs with desired coverage under standard assumptions such as strong ignorability and overlap. In this paper, we show that such goals are fundamentally unattainable. Specifically, we establish finite-sample and asymptotic impossibility results…

二值Logit混合模型的狄利克雷过程在线贝叶斯推断AI 解读

Flexible travel behaviour models for preference heterogeneity have largely remained within the batch-estimation paradigm. The growing availability of continuously collected behavioural data, for example from smartphone applications, highlights the need for methods that update posterior inference on such heterogeneity as new observations arrive. This study proposes an online Bayesian inference method for a Dirichlet process mixture of binary logit models. The main challenge…

变分样条与广义高斯过程AI 解读

Tikhonov regularization, kriging, and Gaussian-process regression share the same algorithm because, for a linear inverse problem with a Gaussian prior and Gaussian noise, the minimum-mean-square-error (MMSE) estimator is the solution of a regularized least-squares problem. We show that this equivalence survives the passage from vectors to generalized functions: the regularized least-squares estimators become generalized splines, and the Gaussian random vectors become…

The synthetic control method (SCM) is widely used for constructing the counterfactual of a treated unit based on data from control units in a donor pool. Allowing the donor pool to contain more control units than time periods, we propose SCM-relaxation, an adaptation of the stable-balancing framework to the synthetic control setting. This approach minimizes an information-theoretic measure of the weights subject to a set of relaxed linear inequality constraints in addition…

从截尾出价中离线学习保留价AI 解读

We study how a seller can learn a reserve price for a second-price auction from censored historical bid data. Because values below past reserves are unobserved, even unlimited data may not reveal which reserve earns the most revenue. Under a monotone hazard rate (MHR) assumption, we characterize exactly when an optimal reserve is identified, even though the value distribution may not be: either revenue has not yet begun to fall at the lowest historical reserve, so the best…

Quadratic moment-based quasi-posteriors depend on a weighting matrix governing both the contributions of moment conditions and the scale of posterior uncertainty. We propose curvature-calibrated quasi-Bayesian (CCQB) updating, which iteratively evaluates the inverse of the estimated moment covariance (or long-run covariance) at the center of the resulting fixed-weight quasi-posterior. A two-part stopping rule monitors changes in the center and Monte Carlo error in estimating…

时变尾部：估计、推断与风险度量AI 解读

Motivated by evidence that the tails of economic and financial time series need not be stable over time, we develop a framework for studying time-varying tails. We model the extreme value index as a smooth function of time in the locally stationary framework of Zhou and Wu (2009). We estimate it by a ratio-form kernel Hill estimator in which a single intermediate quantile thresholds the whole local window. We establish consistency and asymptotic normality, and we construct…

用于变量选择的先验数据拟合网络AI 解读

Prior-data fitted networks (PFNs) have become state-of-the-art for tabular prediction but remain underexplored in other areas of statistics. In this work, we present two PFNs for Bayesian variable selection: CD-PFN identifies conditional dependencies, and PC-PFN identifies parents and children in a causal graph. Both networks estimate posterior inclusion probabilities (PIPs) without posterior samples or data-specific modeling. In semi-synthetic and real data experiments,…

面向因果学习目标的序贯贝叶斯实验设计AI 解读

We present GO-CBED, a goal-oriented Bayesian framework for sequential causal experimental design. Unlike conventional approaches that select interventions aimed at inferring the full causal model, GO-CBED directly maximizes the expected information gain (EIG) on user-specified causal quantities of interest, enabling more targeted and efficient experimentation. The framework is both non-myopic, optimizing over entire intervention sequences, and goal-oriented, targeting only…

个体与整群随机试验中分层生存终点的双稳健胜率统计量AI 解读

Prioritized composite endpoints compare trial participants according to a prespecified clinical hierarchy, and the win estimands are increasingly used to summarize treatment effects. When the component event times are right-censored, a pairwise comparison may remain unresolved, and treating unresolved pairs as ties yields a win statistic whose value depends on the censoring distribution. We define win, loss, and tie probabilities at a fixed restriction time for prioritized…

神经得分驱动的波动率与风险预测AI 解读

We develop a score-driven volatility model in which no particular parametric innovation distribution needs to be specified. The proposed method relies on a lightweight neural network to represent the negative score of the innovation density. By integrating and normalizing the learned score and imposing location-scale identification, we recover a flexible innovation density that can accommodate asymmetry, including negative skewness commonly observed in financial returns. Two…

The persistent volatility of construction material prices poses significant risks to cost estimation, budgeting, and project delivery, underscoring the urgent need for granular and scalable forecasting methods. This study develops a forecasting framework that leverages the Construction Specifications Institute (CSI) MasterFormat as the target data structure, enabling predictions at the six-digit Section level and supporting detailed cost projections across a wide spectrum of…

分布型时间序列的局部Whittle估计：Bayes Hilbert空间中的正则化框架AI 解读

We study local Whittle estimation of long memory in density-valued time series in Bayes Hilbert space. The centered log-ratio (clr) transformation maps densities to a linear Hilbert space, allowing analysis using standard Hilbert space methods. When densities are reconstructed from finite samples, however, small absolute estimation errors can remain large relative to the true density in low-density tails. Taking logarithms can amplify this noise and introduce systematic…

平衡引导森林：因果效应估计AI 解读

Covariate balancing is widely used for estimating causal effects in observational studies. A central challenge is determining which covariate features should be balanced. Forest-kernel balancing provides a data-driven way to learn informative balancing features. Existing forest-kernel balancing methods first learn a tree-based representation and then construct balancing weights. However, this procedure creates a mismatch, as limited tree capacity may be spent on features…

Cluster-randomized trials (CRTs) are used to evaluate group-level interventions and can collect multiple outcomes capturing complementary dimensions of benefit and risk. Investigators typically seek a single global summary of treatment effect, yet existing methods largely focus on single-outcome estimands or rely on model-based procedures with unclear causal interpretation or limited robustness. We develop a unified potential outcomes framework for generalized treatment…

There is a rich literature on clustering functional data with applications to time-series modeling, trajectory data, and even spatiotemporal applications. Existing methods are classified into two categories of global clustering that assigns an identical membership over the whole domain, and local clustering that allows multiple memberships based on the sub-domains. Classical literature has typically focused on global clustering that may struggle with degenerate clustering or…

面向因果效应的去偏条件独立性检验AI 解读

We propose robust tests for causal effects on potentially high-dimensional outcomes where there may be selection into treatment. These tests measure conditional independence of outcomes from treatment assignment by asking whether treatment can be predicted from outcomes more accurately than from covariates alone. We design three such tests of conditional independence. First, we propose a conditional randomization test based on an inverse propensity weighted mean squared…

总体份额下的敏感性分析AI 解读

Empirical work often relies on assumptions on the distribution of random variables, such as potential outcomes or latent variables, to identify some parameter of interest. We introduce a unified framework for assessing the sensitivity of a target estimand to relaxing these distributional assumptions for a given share of the population. We derive bounds on the values the estimand can take as this share varies between 0 and 1, and compute the bounds using tractable…