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双重差分与合成控制的结合：双重稳健识别和估计AI 解读

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition.…

通过边际化狄利克雷过程列聚类与尖峰厚尾稀疏先验实现贝叶斯非参数因子分析AI 解读

We propose a Bayesian nonparametric factor model that infers the number of factors, induces row-wise sparsity, and merges redundant dictionary elements via exact clustering. A Dirichlet process prior is placed on the columns of an overcomplete loading matrix and fully marginalized to an exact Pólya urn, avoiding stick-breaking and auxiliary variables. A spike-and-slab base measure allows entire factors to be exactly zero. The model uniquely combines exact zeros,…

通过最小二乘近似实现自适应 LASSO 惩罚的密度幂散度估计AI 解读

Linear mixed-effect panel data models are widely used in longitudinal biomedical, environmental, and social studies, but are often sensitive to data contamination and numerous covariates. To address these challenges, we propose a robust variable selection approach based on a least squares approximation (LSA) of the density power divergence (DPD) objective function combined with the Adaptive LASSO penalty. The LSA converts the nonlinear DPD objective into a computationally…

非线性政治面板中的预测必要性与因果发现：民主化的反馈和动态AI 解读

A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects averaged toward zero, reciprocal dynamics overlooked by a recursive specification, or relationships masked by the omission of correlated covariates. Standard linear estimators cannot distinguish among these possibilities. We develop an inferential workflow for political panel data that resolves this ambiguity by combining flexible…

含时变中介变量与滞后结果的双重差分模型AI 解读

We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. We allow for the inclusion of lagged outcomes in the model. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the…

用收缩先验建模动态相关矩阵AI 解读

Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse posterior uncertainty. In moderate dimensions, an additional difficulty is summarizing the estimated evolving dependence structure for downstream decision-making tasks. We propose a Bayesian approach based on a low-rank factor representation, with latent states evolving under a dynamic shrinkage…

时间序列共形预测的覆盖率：有效性与推断AI 解读

Conformal prediction provides marginal coverage guarantees, yet practitioners may wonder if the observed coverage is truly abnormal or consistent with sampling variation. Inference for realized coverage has received comparatively little attention, especially for time series. We study split conformal prediction with adjacent calibration and test sets of temporally dependent data. Using the functional dependence measure, we derive non-asymptotic bounds on marginal coverage…

时间序列中有效且高效的分割共形回归AI 解读

We study conformalized quantile regression and conformalized median regression that fit a model on one block of a time series and calibrate the conformal interval on the adjacent block. The existing theory of conformal prediction for time series rests largely on mixing conditions, which are hard to verify from a time-series model and fail for many standard processes, including simple ones with short memory. We replace this theoretical toolbox with the functional dependence…

整群随机试验中依从效应与网络因果效应的识别和有效估计AI 解读

Treatment noncompliance is pervasive in infectious disease cluster-randomized trials. Although all individuals within a cluster are assigned the same treatment condition, the treatment uptake status may vary across individuals due to noncompliance. We propose a semiparametric framework to evaluate the individual compliance effect and network assignment effect within principal stratum exhibiting different patterns of noncompliance. The individual compliance effect captures…

存在干扰时的再随机化AI 解读

Covariates are widely used in randomized experiments to improve precision. However, in the presence of interference, where outcomes may depend on the treatment assignments of other units, standard covariate adjustment methods may fail to preserve desirable properties such as the no-harm property, meaning that incorporating covariates does not worsen estimator performance. Existing approaches that incorporate covariates under interference typically rely on specifying a…

从边际分布识别半鞅的可预测漂移AI 解读

A special semimartingale admits a unique decomposition $X=X_0+M+A$ into a local martingale $M$ and a predictable finite-variation part $A$. We consider the identification of $A$ when $X$ is observed only through repeated cross-sections. The estimand is then the projection of the sampled predictable compensator onto the observable feature filtration, namely the current state together with whatever randomness is shared across the population, so that at a fixed diffusion…

逐步放量即可：网络干扰下 A/B 测试回归估计量的去偏AI 解读

Network interference complicates A/B testing on online platforms, such as social networks and marketplaces, where causal methods based on single experiments often suffer from significant bias due to complex interference patterns. This paper demonstrates the statistical benefits of merging data from multiple experiments with varying treatment proportions. Sequential experimentation with increasing traffic, or ramp-up, is widely used in tech companies for risk management and…

阈值原理、神经网络联系与计量经济学传承札记AI 解读

This is mostly a non-technical note, reflecting the author's philosophy and personal views on time series analysis, with references limited to only a few representatives for brevity. The Threshold Principle, formally announced in Tong (1990), modernised time series analysis by introducing a collection of sub-systems to model complex nonlinear dynamics. We explore the conceptual architecture of the Threshold Principle drawing parallels and contrasts with artificial neural…

切片 Lp 分布平衡方法AI 解读

A popular class of causal inference methods addresses confounding through weighting, which reweights treated and control groups to balance their covariate distributions without using outcome information, thereby preserving a design-based perspective. In this paper, we propose sliced $L^p$ distributional balancing (SLDB), a family indexed by $p\in[1,\infty)$ that measures imbalance by averaging squared $L^p$ distances between the cumulative distribution functions of…

表格基础模型能否摊销统计推断成本？AI 解读

For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians design a problem-specific estimator together with a procedure for quantifying its uncertainty. This paper proposes a different paradigm. We focus on a classical problem in statistical inference, confidence interval construction, and develop TabCon, an amortized inference system built on a tabular…

通过团搜索识别非线性且相关的潜在因子结构AI 解读

Learning the structure of latent factor models involves two central challenges: (1) estimating the number of latent factors and (2) learning the support of the mapping from latent variables to observed variables. This is especially challenging for nonparametric regimes and nonlinear settings. We propose a method for latent structure learning, based on pairwise dependence measures on the observed variables using a graph-theoretic representation. We show that both the number…

线性模型中的最优检验及其因果推断含义AI 解读

Despite its widespread use in practice, multiple regression suffers from two important limitations due to ignoring the structure between predictors. First, reported coefficients are, in fact, direct effects in a causal context. These are often less meaningful than total effects when accounting for a factor's contribution, however, estimating the latter typically requires a causal diagram to be specified among predictors. The second limitation is multicollinearity, which…

线性不等式约束下投影几何的极小极大选择AI 解读

Economic theory frequently implies linear inequality restrictions on parameters or functions of interest. A common way to impose such restrictions is to project an unrestricted estimator onto the feasible set. Projection estimators arise naturally from constrained least squares, instrumental variables, generalized method of moments, maximum likelihood, and related extremum procedures. When the sampling covariance, loss function, and projection criterion induce different…

Hájek 卷积定理的二阶扩展及统计应用AI 解读

For a class of regular estimators, Hájek, in his celebrated ``Convolution Theorem,'' showed that the asymptotic distribution of a regular estimator is the convolution of the distribution of an efficient estimator and some residual distribution. This result constitutes the foundation of the concept of asymptotic efficiency of regular estimators. In this paper, we provide a second-order version of that classical result. Introducing a class of second-order regular estimators…

稠密精度矩阵估计的非渐近框架：复杂度、信号与双重下降AI 解读

Precision matrix estimation is a cornerstone concept in statistics, economics, and finance. Despite advances in recent years, estimation methods that are simultaneously (i) dense, (ii) consistent, and (iii) model-free are lacking. While each of these targets can be met separately, achieving them together is challenging.We address this gap by introducing a general class of estimators that unifies these features within a nonasymptotic framework, allowing for explicit…

弱依赖估计方程的两点依赖野生自助法AI 解读

This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid…

协变量依赖删失下半竞争风险数据的双重稳健估计AI 解读

Semi-competing risks occur when individuals may experience a non-terminal event and a terminal event, where the terminal event censors the non-terminal event but not vice versa. In the presence of covariate-dependent censoring, augmented inverse probability of censoring weighting (AIPCW) framework has not been developed for the special setting of semi-competing risks which, unlike competing risks, have asymmetric event time structure and complex estimands. Using…

基于负二项边际与高斯 Copula 的重复计数结局参照组敏感性分析AI 解读

Reference-based multiple imputation is used in longitudinal clinical trials to assess sensitivity to assumptions about outcomes unobserved after intercurrent events. Most existing methods target continuous outcomes and use multivariate normal working models. Scheduled count outcomes require a model that preserves integer support, skewness, overdispersion, longitudinal dependence, and an exposure-based rate interpretation. We propose visit-specific negative binomial (NB)…

似然模型中的正交矩AI 解读

Many models, such as fixed-effect models for panel or network data, are hard to estimate because they feature nuisance parameters that are both numerous and estimated imprecisely. This, in general, causes an incidental-parameter problem in the estimator of the parameters of interest. The problem can be alleviated by working with an estimating equation whose expectation is insensitive to the value of the nuisance parameters. We discuss and contrast three notions of…