每日简报

迁移学习与自适应 LASSO 分位数回归AI 解读

We propose for a quantile regression an estimation method for transferring knowledge using two $L_1$ penalties based on an estimator obtained from a source database. The proposed transfer learning estimator satisfies the properties of consistency and sparsity. Its convergence rate and asymptotic behavior are studied in several scenarios. This knowledge transfer results in a shorter computation time than that of the standard adaptive LASSO estimator. Another advantage of our…

结合分布方法的单侧与双侧混合数据风险差置信区间AI 解读

Combined unilateral and bilateral binary outcomes frequently arise in studies involving paired organs. The risk difference is a clinically interpretable measure for comparing treatment effects between groups. Existing confidence interval methods are primarily based on asymptotic normality and may fail to adequately reflect finite-sample distributional features, particularly skewness. To address this issue, we propose a distribution-based confidence interval derived from the…

真实世界证据中信息性治疗切换的工具变量方法AI 解读

Reproducible and generalizable assessment of treatment decisions requires principled handling of subsequent treatment switching that may inform expected outcomes and shift across cohorts and over time. To effectively account for informative treatment switching, we propose an instrumental variable approach that characterizes the poorly documented expected outcomes at switching as unmeasured confounding. After establishing the baseline treatment as a viable instrumental…

流动性溢价与投资期限AI 解读

We estimate Kyle's (1985) price-impact coefficient $λ$ directly from daily equity order flow and test its ability to forecast the cross-section of subsequent stock returns. Using CRSP data from 2020 to 2025, we construct firm-month measures of signed order flow and two estimators of $\hatλ_{it}$: a within-month price-impact regression and an Amihud-style ratio. Signed order flow strongly predicts contemporaneous and one-month-ahead returns, while volume volatility predicts…

网络结构的选择后推断AI 解读

Researchers often use the density of connections between groups of agents, such as communities, blocs, or markets, to characterize the structure of a social or economic network. In many cases, these groups are selected using the network data, making conventional fixed-group inference procedures potentially invalid. To address this issue, we develop two new confidence intervals that are universally valid post-selection in the sense that they guarantee simultaneous coverage…

针对排除限制与外生性违背的广义证伪适应集AI 解读

The falsification adaptive set (FAS) as proposed by Masten and Poirier (2021) provides an identified set for a treatment effect when the baseline model is falsified, assuming invalid instruments violate exclusion only. We show that whether an invalid instrument is a confounder or collider has important consequences: incorrect treatment can cause the FAS to exclude the true parameter. We derive pattern-specific falsification adaptive sets for each combination of violations…

边际处理效应函数的一致置信带AI 解读

This paper presents a method for constructing uniform confidence bands for the marginal treatment effect (MTE) function. The shape of the MTE function provides insight into how the unobserved propensity to receive treatment relates to the treatment effect. Our approach visualizes the statistical uncertainty of an estimated function, facilitating inferences about the function's shape. The proposed method is computationally inexpensive and requires only minimal information:…

时间序列回归的稀疏树状聚合AI 解读

High-dimensional time series regressions are often regularized to produce sparse coefficients. We show that temporal aggregation provides a powerful alternative to reduce dimensionality in high-order autoregressions and mixed-frequency regressions. To this end, we propose StarTime (Sparse Tree-based Aggregation for Time Series), a convex penalization method that uses a temporal tree to arrange lags hierarchically from high to low frequency. StarTime then flexibly selects…

序贯实验的半参数效率：基于平均倾向得分的刻画与设计AI 解读

Modern experiments, including evaluations of AI-enabled services and platform interventions, often depart from independent and identically distributed (i.i.d.) sampling because assignments may be adaptive, balanced across covariates, or subject to rollout constraints such as exposure, fairness, and budget limits. This paper studies the efficiency benchmark for estimating causal targets in such sequential experiments. We show that every non-anticipating design induces an…

利用均值偏移惩罚检测状态空间模型中的异常值AI 解读

State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We propose a robust estimation method, ROAMS, that mitigates the influence of additive outliers by introducing shift parameters at each timepoint in the observation equation of the SSM. These parameters allow the model to attribute non-zero shifts to outliers while leaving clean observations…

测量误差诱发的混杂AI 解读

A critical assumption of observational studies is that all confounding variables must be known and sufficiently adjusted for to estimate causal effects. An implicit, and often overlooked, aspect of this assumption is that all confounding variables have been measured without error. In the social and medical sciences, latent traits such as motivation, self-efficacy, and ability measures are likely confounding variables. Because latent traits are not directly observable,…