每日简报

目标函数的目标化高度自适应Lasso最小损失估计AI 解读

We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We assume the target function lies in the $k$-th order smoothness class used to define the $k$-th order Highly Adaptive Lasso (HAL), which can be well approximated by linear spans of $k$-th order spline basis functions. We construct a projection of the true target function onto a large finite…

普吉特海湾地区在线购物行为的伪面板双重差分分析AI 解读

Online shopping is a growing trend, particularly following the COVID-19 lock-downs enacted by many cities. Understanding these trends requires robust panel dat methods. However, panel data (i.e., with repeated measurements of the same observational units) are often unavailable. In this study, we use a propensity score weighting (PSW) approach to adjust repeated cross-sectional travel diary surveys collected in the Puget Sound Regional Council (PSRC) region. The most common…

远程办公：数字产品创新的推动力还是阻碍？AI 解读

As firms adopt divergent policies regarding work-from-home (WFH), the implications of remote work for collaborative and interdependent outcomes such as digital product innovation remain uncertain. This study examines how remote work adoption affects continuous digital product innovation using a panel dataset of mobile applications. We identify firm-level remote work adoption from job postings data and estimate its effects on app innovation using a staggered…

移动平均与自回归过程的似然几何AI 解读

We study the problem of maximum likelihood estimation for moving average (MA) time series models from the perspective of algebraic statistics, with a focus on the structure and number of solutions of their likelihood equations. Of particular interest is to classify the critical points that lead to non-invertible models. We consider the composite likelihood as an alternative estimation method and analyze its critical points. We extend our algebraic analysis to autoregressive…

谱边界上的理性泡沫：脆弱性、识别与有限样本认证AI 解读

When markets move more and more in lockstep, are they drifting towards the point where a price bubble becomes possible, and can that drift be measured before the crossing? This paper joins two long-separate ideas, that a rational bubble is a price outgrowing its dividends and that a crisis threshold can be read off the strength of a market's single dominant factor, onto one object recovered from the data: a summary of how asset returns move together, paired with a discount…

尾部局部高斯相关：样本稀缺诊断、最优局部带宽与适应性的边界AI 解读

Local Gaussian correlation (LGC) measures dependence locally, making it a natural tool for tail dependence and financial contagion, but its estimates degrade in the joint tails, where they are most needed. Location-adaptive bandwidths have been tried for LGC and found inferior to a single global bandwidth; we explain why, and map the regime in which adaptivity does help. First, a diagnostic: across heavy-tailed data-generating processes the parametric marginal pre-transform…

显著性优先分裂：协调处理效应异质性检测与诚实估计AI 解读

Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off: significance-based approaches (Radcliffe and Surry 2011) identify subgroup interactions directly but lack valid inference; honest causal trees (Athey and Imbens 2016) deliver nominal confidence interval coverage but use outcome-agnostic splitting criteria that sacrifice…

B-CALM：协变量不匹配下以随机试验为锚的偏差受限贝叶斯信息借用AI 解读

Randomized controlled trials (RCTs) identify treatment effects in the randomized trial population but are often too small for reliable heterogeneity estimation; observational studies (OS) are larger but confounded and measured on only partially overlapping covariates. We develop Bayesian Calibrated ALignment under covariate Mismatch (B-CALM), a Bayesian borrowing method for RCT-defined conditional average treatment effect (CATE) estimation. B-CALM maps source-specific…

稀疏线性回归的高效交叉验证AI 解读

Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner. To choose hyperparameters that control the sparsity level and amount of regularization, practitioners commonly use k-fold cross-validation. However, cross-validation substantially increases the computational cost of sparse regression as it…

样本个体数为一时的实验设计AI 解读

N-of-1 trials, or time-series experiments, are widely used in clinical research and online platforms. Yet the theoretically optimal design for estimating many treatment effects remains unclear. We propose a simple Markovian framework for experimental design in which the treatment assignment process is governed by possibly time-varying transition matrices. This formulation encompasses many existing N-of-1 designs and provides a principled way to control temporal dependence in…

具有设计型方差的交叉拟合调查加权 TMLEAI 解读

Cross-fitting is not a refinement of survey-weighted causal machine learning but, once the nuisances are flexible, what restores valid inference. We study the population average treatment effect under a stratified multistage design, estimated by a survey-aware targeted maximum likelihood estimator (TMLE) whose variance is obtained by Taylor-series linearization of the influence function, treating the primary sampling unit as the replication unit. Our central result is that…