每日简报

基于流行度回归的潜空间模型：大规模稀疏网络分析AI 解读

Degree heterogeneity is one of the most important properties of network data. It is widely observed that degree heterogeneity is often related to the nodal features. In this study, we investigate the estimation and statistical inference for a popularity regression-based latent space model with nodal features. Given the complex dependence structure induced by the latent space model, we aim to derive analytically tractable objective functions that effectively account for this…

Switchback experiments are widely used in pricing applications. We consider a retailer running such an experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices consisting of an original price and a few discounted price levels. The goal is to estimate the demand gradient at the original price to adjust the original price and improve revenue after the experiment. In our model, in each period,…

每个站点样本较少的多站点随机实验中治疗效应异质性的估计AI 解读

Multi-site randomized experiments, which are common in economics and medicine, can reveal how treatment effects vary across settings. However, differences between site-specific estimates reflect both treatment-effect heterogeneity and estimation noise. We build on established measurement-error corrections and develop new ones to study this heterogeneity in experiments with many sites and few units per site. In particular, our results can be used to estimate the variance and…

处理效应的覆盖范围：零效应区域的随机化推断AI 解读

A treatment may affect some individuals while leaving others unaffected. We study the reach of treatment effects by asking how large an unaffected covariate region could be. We develop efficient local randomization tests over nearest-neighbour regions to construct a finite-sample valid upper confidence bound for the size of the largest zero-effect region. A small bound provides evidence that treatment effects extend broadly across covariate values by ruling out large…

阶梯楔形整群随机试验中总体与亚组因果效应的校准贝叶斯推断AI 解读

Stepped-wedge cluster-randomized trials (SW-CRTs) randomly assign clusters to sequences that determine when each cluster switches from control to intervention. Conventional Bayesian analyses of SW-CRTs report treatment effect coefficients from a working outcome model, but under model misspecification or informative cluster-period sizes, such coefficients can lack a clear causal interpretation. In this work, we develop Bayesian methods targeting weighted average treatment…

面向删失结局的灵活双稳健近端因果推断AI 解读

Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses unmeasured confounding through proxy variables, but existing proximal survival methods rely largely on restrictive parametric bridge models. We introduce Proximal Survival Bridge learning (PSB), a flexible doubly robust proximal framework for censored outcomes. Building on existing proximal identification of treatment-specific…

因子辅助密度估计：当环境维度带来神谕速率AI 解读

We study density estimation in the high-dimensional factor model, whose fixed-dimensional latent factors have an unknown smooth density. We propose factor-assisted density estimation (FADE), a non-iterative procedure that applies kernel density estimation to spectral estimates of the latent scores. The score proxies are dependent and do not follow the latent density, so standard kernel-density arguments do not apply. A refined rowwise expansion separates error into a…

基于超阈值-协变量独立性的尾指数同质性检验AI 解读

We propose a test of whether the tail index of a heavy-tailed outcome is homogeneous across observed covariates. The key observation is that, under tail-index homogeneity, normalized log exceedances are asymptotically independent of the covariates even when tail frequencies vary with them. This leads to a simple-to-implement permutation test based on distance covariance, without estimating conditional tail indices or specifying the form of tail-index heterogeneity. For the…

超越比赛结果：职业网球中能力、表现与获胜的贝叶斯中介分析AI 解读

Match results measure competitive ability, but they do not explain how ability is expressed during play. Match statistics offer a natural mediator, however performance is usually measured over the same match as the outcome: a full-match statistic uses events that help determine the winner, while a first-set statistic is earlier but rests on fewer service points and still contributes to the result. Comparing the two therefore mixes timing and precision. We develop a Bayesian…

利用异方差在隐藏混杂下进行因果发现AI 解读

The presence of hidden confounding in observational studies poses a fundamental challenge to the identification and estimation of causal structures encoded by directed acyclic graphs (DAGs). While instrumental variables (IVs) have been widely used to address hidden confounding, most existing methods require valid instruments, which may be unavailable in practice. In this paper, we develop a novel framework for causal discovery and inference in DAGs that accommodates both…

双向固定效应面板数据的因果中介分析：双重/去偏机器学习方法AI 解读

We develop a double/debiased machine learning framework for causal mediation analysis in panel data with two-way fixed effects. Binary, multivalued, or continuous treatments and continuous mediators affect outcomes via histories. We formulate partially linear models for potential outcomes and mediators, define history-specific direct, indirect, and total effects, and identify them under a sequential conditional independence assumption given unit effects, allowing arbitrary…

校准迁移学习用于生存预测与治疗推断AI 解读

Historical survival data can improve prediction in a cohort with few events, but prognostic differences can make borrowing harmful. To borrow while adapting, we propose calibrated transfer learning for the Cox model, in which a historical risk function enters the target analysis as a fixed offset with a prespecified correction for changing covariate effects and a separate baseline hazard. Calibration removes historical error in the correction space exactly, so only the…

机器学习预测结果下的局部稳健推断AI 解读

Many parameters of interest in economics depend on outcomes generated by machine learning (ML).These generated outcomes are subsequently used in standard econometric estimation and typically treated as observed, a practice that threatens the validity of downstream inference. Empirically relevant cases include dynamic treatment effects under sequential exogeneity, long-term effects with surrogates, the partially linear model with an ML-predicted outcome, productivity…

基于最优传输的稳定因果估计AI 解读

In observational studies, the overlap assumption, also called positivity, is frequently strained in practice. Limited overlap destabilizes widely used estimators such as inverse propensity weighting (IPW), whose variance depends on density ratios that explode as propensity scores approach zero or one. To address this challenge, we propose the Stable Transport Estimator (STE), an optimal transport-based approach for estimating average causal effects when overlap holds but…

Extrapolating treatment effects from related studies is a promising strategy for designing and analyzing clinical trials in situations where achieving an adequate sample size is challenging. Bayesian methods are well-suited for this purpose, as they enable the synthesis of prior information through the use of prior distributions. While the operating characteristics of Bayesian approaches for borrowing data from control arms have been extensively studied, methods that borrow…